A cautionary note: I’m being flippant here in several ways. First, the list of behaviors in this paragraph is vertebrate-centric. Second, it’s unlikely that any individual vertebrate alters behavior very much in its own lifetime; big changes in behavioral strategy occur over generational time.
Scoring points by helping your relatives raise kin is formally recognized as “inclusive fitness.”
Although it probably seems crazy at first, I’m not sure that evolving robots aren’t alive. The quality that we recognize scientifically as “life” is usually a suite of characteristics that include the ability of a self-contained entity to (1) make additional and similar versions of itself, (2) gather energy, (3) convert and use the gathered energy to perform chemical and mechanical work (e.g., build or repair itself, gather more energy, make copies of itself), and (4) decrease entropy (disorder) locally and temporarily. These ideas are influenced, in part, by the physicist Erwin Schrödinger in his 1944 book, What Is Life? (based on lectures delivered under the auspices of the Dublin Institute for Advanced Studies, Dublin, February 1943). From cognitive science, I would include, in our life list, the ability of a self-contained entity to (5) react to changes in the patterns of the global energy array with the goal of reproducing and gathering energy. What do you think?
This definition of natural selection is a bit different from that found in textbooks. In Mark Ridley’s excellent textbook, Evolution (3rd ed.)(Malden, MA: Blackwell Science, 2004), he enumerates Darwin’s four necessary and sufficient conditions for natural selection: (1) reproduction, (2) heredity, specifically, offspring resembling parents, (3) variation in individual characters among members of the population, and (4) that variation in reproductive output for individuals is tied to the variation in characters. Note here that the concept of a population is secondary; I make it primary in order to emphasize the concept of interaction of individuals and their world being defined by who they are with and where they are.
You can take this journey by reading Jonathan Weiner’s book The Beak of the Finch: A Story of Evolution in Our Time (New York: Alfred A. Knopf, 1994).
This insight—that slow, small changes happening right in front of us are sufficient to drive large-scale and dramatic changes over long periods of time—is the basis for explaining any kind of evolutionary change. When we’ve published evolutionary simulations with robots that measure change over generational time, we claim that we are learning something about the evolutionary processes that have occurred over millions of years to help create new traits and new species.
John Stuart Mill created methods for inferring causal relations that includes ceteris paribus. Mill’s methods and other reasoning techniques are explained in an accessible manner in David Kelley’s The Art of Reasoning, 3rd ed. (New York: W. W. Norton & Company, 1998).
Robert Brandon, Adaptation and Environment (Princeton, NJ: Princeton University Press, 1990).
Barbara Webb, “Can Robots Make Good Models of Biological Behaviour?” Behavioral and Brain Sciences 24, no. 6 (2001): 1033–1055.
Richard Feynman, the Nobel Laureate in Physics, said something similar: “What I cannot create, I do not understand.”
What’s really useful about the secret code is that it implies that if you can’t build it (whatever “it” is), then you don’t really understand it. This is another way of thinking about what people call an “existence proof,” which is the ultimate in physical evidence: if it exists, then it can exist.
For an in-depth examination of representation, see Tim Crane’s Mechanical Mind: A Philosophical Introduction to Minds, Machines, and Mental Representation, 2nd ed. (London, New York: Routledge, 2003).
In other words, make a plan. Once you have an explicit plan researched and written down—the answers to the six design questions are a good start—then keep in mind what General Dwight Eisenhower said: “In preparing for battle I’ve always found that plans are useless, but planning is indispensible.”
If you are interested, these issues are addressed in the fast-paced fields of phylogenetics, phylogenomics, and evolutionary developmental biology.
In the mid-naughties—2000s—the flux in our understanding of evolutionary relationships was widely recognized in college-level textbooks. For an excellent example, see Chapter 1 in Michael J. Benton, Vertebrate Paleontology, 3rd ed. (Malden, MA: Blackwell Science, 2005). Also, Frédéric Delsuc, Henner Brinkmann, Daniel Chourrout, and Hervé Philippe, “Tunicates and Not Cephalochordates Are the Closest Living Relatives of Vertebrates,” Nature 439, no. 7079 (2006): 965–968. This then-radical view has been tentatively accepted (but still subject to revision as new data are generated), particularly in light of new information on the genome of cephalochordates. See Peter W. H. Holland, “From Genomes to Morphology: A View from Amphioxus,” Acta Zoologica 91, no. 1 (2010): 81–86.
Tunicates (also called ascidians and urochordates) are members of the Phylum Chordata, a group of related species that also contains vertebrates and lancelets. Lancelets, also called amphioxus (species name Branchiostoma), are members of the Cephalochordata. For an excellent comparison of these two groups in the context of vertebrate origins, see M. Schubert, H. Escriva, J. Xavier-Neto, and V. Laudet, “Amphioxus and Tunicates as Evolutionary Model Systems,” Trends in Ecology and Evolution 21, no. 5 (2006): 269–277.
The phrase “ugly bags of mostly water” was a first-contact description of humanoids uttered by a crystalline life-form in the Star Trek: The Next Generation episode “Home Soil” (season 1). If these crystalline life-forms had seen adult tunicates and humanoids side by side, I’m guessing that they would’ve called the former bags and the latter, more accurately, tubes or branched cylinders.
Walter Garstang, “The Morphology of the Tunicata, and Its Bearing on the Phylogeny of the Chordata,” Quarterly Journal of Microscopical Science 62 (1928): 51–187. In one of those beautiful, accidental collusions between science and art, his scientific ideas are immortalized in his poetry, published posthumously in 1951 as Larval Forms and Other Zoological Verses (Oxford: Blackwell).
You can read about Tadro1 in J. H. Long Jr., C. Lammert, C. A. Pell, M. Kemp, J. Strother, H. C. Crenshaw, and M. J. McHenry, “A Navigational Primitive: Biorobotic Implementation of Cycloptic Helical Klinotaxis in Planar Motion,” IEEE Journal of Oceanic Engineering 29, no. 3 (2004): 795–806.
J. H. Long Jr., T. J. Koob, K. Irving, K. Combie, V. Engel, N. Livingston, A. Lammert, and J. Schumacher, “Biomimetic Evolutionary Analysis: Testing the Adaptive Value of Vertebrate Tail Stiffness in Autonomous Swimming Robots,” Journal of Experimental Biology 209, no. 23 (2006): 4732–4746.
You can see the original pictures of the fossils in this paper: D.-G. Shu, S. Conway Morris, J. Han, Z.-F. Zhang, K. Yasui, P. Janvier, L. Chen, X.-L. Zhang, J.-N. Liu, Y. Li, and H.-Q. Lui, “Head and Backbone of the Early Cambrian Vertebrate Haikouichthys,” Nature 421, no. 6922 (January 2003): 526–529.
Sindre Grotmol, Harald Hryvi, Roger Keynes, Christel Krossøy, Kari Nordvik, and Geir K. Totland, “Stepwise Enforcement of the Notochord and Its Intersection with the Myoseptum: An Evolutionary Path Leading to Development of the Vertebra?” Journal of Anatomy 209, no. 3 (2006): 339–357.
“Stiffness” by itself is an ambiguous term. Many different kinds of stiffness exist. What they have in common is that they are all a proportionality constant between an applied force, stress, or torque and the resulting change in length, strain, or curvature, respectively. Flexural stiffness is defined as the proportionality constant between a torque and the resulting curvature. Flexural stiffness assumes that this relationship is the same all along the length of the structure that you are bending. If you want to describe the stiffness of the whole structure, a physicist would talk about “spring constant” or a “spring stiffness.” I prefer the term “structural stiffness.” In the case of a cantilevered beam with a weight hung off the end, the structural stiffness is the ratio of the flexural stiffness to the cube of the beam’s length. This means that you can have beams of constant flexural stiffness but different structural stiffness if you vary their lengths.
For reconstructions of the skeletons and their partial vertebrae, see J. A. Long and M. S. Gordon, “The Greatest Step in Vertebrate History: A Paleobiological Review of the Fish-Tetrapod Transition,” Physiological and Biochemical Zoology 77, no. 5 (2004): 700–719.
The use of the term “biomimetic” varies across engineering, bioengineering, and biomedical engineering. Here I use “biomimetic” to mean a system built to resemble, as much as possible, the biological target.
Rolf Pfeifer, and Christian Scheier, Understanding Intelligence (Cambridge, MA: MIT Press, 1999).
Hou Xian-Guang, Richard J. Aldridge, Jan Bergstron, David J. Siveter, Derek J. Siveter, and Feng Xiang-Hong, The Cambrian Fossils of Chengjiang, China: The Flowering of Early Animal Life (Malden, MA: Wiley-Blackwell, 2007).
Yes, I’m referencing the television show Survivor on CBS. Their logo reads, “Outwit, outplay, outlast.” I in no way mean to imply that reproduction is or should be part of this show.
Cameron K. Ghalambor, Jeffrey A. Walker, and David N. Reznick, “Multi-trait Selection, Adaptation, and Constraints on the Evolution of Burst Swimming Performance,” Integrative and Comparative Biology 43, no. 3 (2003): 431–438. Also see R. B. Langerhans, “Predicting Evolution with Generalized Models of Divergent Selection: A Case Study with Poeciliid Fish,” Integrative and Comparative Biology 50, no. 6 (2010): 1167–1184.
Barbara Webb, “Can Robots Make Good Models of Biological Behaviour?” Behavioural and Brain Sciences 24, no. 6 (2001): 1033–1050. Also see Webb, “Validating Biorobotic Models,” Journal of Neural Engineering 3, no. 3 (September 2006): R25–R35. I use my rephrased terms of Webb’s dimensions in my paper “Biomimetic Robotics: Self-Propelled Physical Models Test Hypotheses about the Mechanics and Evolution of Swimming Vertebrates,” Proceedings of the Institution of Mechanical Engineers, Part C, Journal of Mechanical Engineering Science 221, no. 10 (2007): 1193–1200.
Plagiarism detector alert: a thank you to Lewis Carroll.
This single number that measures feeding behavior is a composite from the fitness function that we developed in Chapter 3. We defined better “relative fitness” for an individual in a given generation, relative to other individuals in that generation, as the sum of their scaled values for increased swimming speed, decreased time to the light target, reduced distance from the light target over the course of the whole experiment, and reduced wobble as they moved. These relative fitness values only make sense within a generation relative to other competing individuals at that time and place: they can’t be compared across generations. To make those cross-generation comparisons for Figure 4.1, we compared any individual’s performance to the average of all individuals over all ten generations scaled by the standard deviation of the particular sub-behaviors, speed, time, distance, and wobble. In statistical terms, we summed up the z-scores of each sub-behavior for each individual.
In statistics one standard deviation, which changes in value depending on the situation, is a measure of how far away from the average most numbers in a group of numbers fall. A small standard deviation means that most numbers in the group are close to the average of the group.
If you are interested in the mathematics of the mating that we used, you can find the details in our paper on the evolution of Tadro3: J. H. Long Jr., T. J. Koob, K. Irving, K. Combie, V. Engel, N. Livingston, A. Lammert, and J. Schumacher, “Biomimetic Evolutionary Analysis: Testing the Adaptive Value of Vertebrate Tail Stiffness in Autonomous Swimming Robots,” Journal of Experimental Biology 209, no. 23 (December 2006): 4732–4746.
In John Gillespie’s Population Genetics: A Concise Guide, 2nd ed. (Baltimore: Johns Hopkins University Press, 2004), he speaks of “demographic stochasticity” as this source of small-number randomness. He also points out a second such source, the segregation of the different parental alleles into separate gametes. Both sources together he calls genetic drift. In our robotic simulation segregation is not a factor because our quantitative characters are, by design, split evenly between chromosomes.
Students and scholars of evolutionary theory will be quick to interject, what about sexual selection, gene flow, genetic drift, epistasis, mating, and developmental processes as evolutionary mechanisms? True. Those are other identifiable mechanisms of evolutionary change. Lenski’s point, which I follow here, is that any mechanism fits into a category of either being deterministic or random. Natural selection is deterministic in that once you identify all of Brandon’s information (see Chapter 2), you can predict evolutionary outcome. Random factors like mutation or assortative mating have outcomes that are not predictable. I continue to be influenced by this fascinating and illuminating paper: M. Travisano, J. A. Mongold, F. Bennett, and R. E. Lenski, “Experimental Tests of the Roles of Adaptation, Chance, and History in Evolution,” Science 267, no. 5194 (1995): 87–90. Also, you may be interested in the Neutral Theory of molecular evolution, which is based on the idea that most random genetic changes have no effect on selection. In the face of genomic data, this idea is rapidly changing: Matthew W. Hahn, “Toward a Selection Theory of Molecular Evolution,” Evolution 62, no. 2 (2007): 255–265.
For completeness, I should tell you that the variable I (in units of meters to the fourth power) is called the “second moment of area.” The second moment of area is a geometric property of how the structure’s material is arranged and clustered in cross-section, the plane perpendicular, to, in this case, the long axis of our beam that we measure with the variable L.
You can find a great introduction to the evidence for our mental modeling in the following book: Read Montague, Your Brain Is (Almost) Perfect: How We Make Decisions (New York: Plume, 2006).
The philosopher of science, Karl Popper, has formalized the “hypothetical-deductive” methodology in order to avoid what other philosophers have called the “problem of induction,” or generalizing from a few observations to the world in general. Most of our statistical hypothesis testing in science is structured around the idea of falsification or rejection of the “null” hypothesis. The danger with this approach is that if you reject the null hypothesis, you are tempted to treat the alternative as “true,” when in fact it becomes the new null to be tested. An excellent place to start with this kind of careful inference is with Popper himself: Karl R. Popper, The Logic of Scientific Discovery (New York: Basic Books, 1959).
For that matter (ahem …), no one has seen energy. In fact, physicists don’t even know what energy is. Richard Feynman, the Nobel Laureate in Physics, and his coauthors Robert Leighton and Matthew Sands point this out eloquently in The Feynman Lectures on Physics, vol. 1 (Reading, MA: Addison-Wesley, 1964), 4-2.
I’ve given short shrift here to an interesting philosophical debate: logical positivism versus Popper’s hypothetico-deductivism. One distillation of the difference is modus ponens versus modus tollens logic, respectively.
In case you are interested in what we did to try to find our flaws, here’s an example. We were very concerned that our initial measurements of structural stiffness were somehow flawed. We had created a standard curve that gave us a value of material stiffness, E, for a given amount of gelatin and cross-linking time. We retested that formula to make sure it was accurate. Moreover, if our method for making and measuring the biomimetic notochords was highly variable, that would be an additional source of random variation and noise. To test this we split up into three different groups and completely remade all of our tails, and then we tested them for structural stiffness using a materials testing device. We compared the three groups for inter-rater reliability, the level of agreement between us. In the worst case the correlation of our stiffness measurements between groups was 0.91 out of 1.0.
In statistics-ese these least-squares linear regressions are all “highly significant,” with p < 0.01 in each case. Prior to testing, all data were transformed so that they fit a normal distribution. The 20 percent refers to the “coefficient of determination,” also called the “r-squared value,” a number from 0 to 1 that indicates how well the best-fit line represents the relationship between the dependent and independent variables.
Warning: “epistasis” has different meanings. For example, Gillespie (Population Genetics) defines three different kinds of genetic epistasis. Here I am taking the broader view of interactions among genes impacting fitness, similar to “functional epistasis.” I’m interested in the gene-to-fitness mapping mediated by phenotype. In Tadro3 wobble and speed interact. Because both are correlated with stiffness, and stiffness is genetically coded, selection on wobble and speed alter the genetics of the population. Simulations show that epistatic networks can adapt: Roman Yukelevich, Joseph Lachance, Fumio Aoki, and John R. True, “Long-Term Adaptation of Epistatic Genetic Networks,” Evolution 62, no. 9 (2008): 2215–2235.
In case your bad-grammar detector has signaled, I should explain that I’m trying to make a self-referential joke.
Cameron K. Ghalambor, Jeffrey A. Walker, and David N. Reznick, “Selection, Adaptation and Constraints on the Evolution of Burst Swimming Performance,” Integrative and Comparative Biology 43, no. 3 (2003): 431–438.
Rowan D. H. Barrett, Sean M. Rogers, and Dolph Schluter, “Natural Selection on a Major Armor Gene in Threespine Stickleback,” Science 322, no. 5899 (2008): 255–257.
Richard W. Blob, Sandy M. Kawino, Kristine N. Moody, William C. Bridges, Takashi Maie, Margaret B. Ptacek, Matthew L. Julius, and Heiko L. Schoenfuss, “Morphological Selection and the Evaluation of Potential Tradeoffs Between Escape from Predators and the Climbing of Waterfalls in the Hawaiian Stream Goby Sicyopterus Stimpsoni,” Integrative and Comparative Biology 50, no. 6 (2010): 1185–1199, doi:10.1093/icb/icq070.
This ability to “know” or infer if any other agent, organic or artificial, possesses a mind is an exciting area of philosophical and scientific work that’s usually called, “The Problem of Other Minds.” I realize that I’m conflating “mind” and “intelligence” here. The two are often treated as interchangeable: a human mind is considered, by definition, intelligent; thus, so it goes for some, intelligence is found only in human minds.
Interaction of a human and a potential artificial intelligence is the basis of what Turing called the “imitation game.” We now call this the “Turing Test.” Read all about it in his wonderfully accessible paper: Alan Turing, “Computing Machinery and Intelligence,” Mind 59, no. 1 (1950): 433–460.
Here’s the official site of the Loebner Prize: www.loebner.net/Prizef/loebnerprize.html.
Stevan Harnad, “The Turing Test Is Not a Trick: Turing Indistinguishability Is a Scientific Criterion,” SIGART Bulletin 3, no. 4 (October 1992): 9–10.
Impossible as the T3 may seem to us as we contemplate human-level performance, I argue that the T3 has been passed, perhaps even at the level of the Loebner Prize gold medal, for a different species: cockroaches. Autonomous cockroach robots fooled real cockroaches so well that they could cause the real cockroaches to do things they didn’t normally do, like form groups in the light (they prefer the dark). Here’s the brilliant paper: J. Halloy et al., “Social Integration of Robots into Groups of Cockroaches to Control Self-Organized Choices,” Science 318, no. 5853 (November 2007): 1155–1158.
This paper contains an excellent description of Searle’s classic “Chinese room” thought experiment: John Searle “Is the Brain’s Mind a Computer Program?” Scientific American 202, no. 1 (1990): 26–31.
Experimental evidence for self-recognition in dolphins can be found in this paper: D. Reise and L. Marino, “Mirror Self-Recognition in the Bottle-Nose Dolphin: A Case of Cognitive Convergence,” Proceedings of the National Academy of Sciences 98, no. 10 (2001): 5937–5942.
H. M. Gray, K. Gray, and D. M. Wegner, “Dimensions of Mind Perception,” Science 315 (2007): 619.
In 1982 Vassar College became the first institution in the world to offer an undergraduate major in cognitive science. Hampshire College disputes Vassar’s claim of primacy (see the claim on the website of their School of Cognitive Science at www.hampshire.edu/cs/).
A terrific exploration of embodied intelligence is Louise Barrett’s Beyond the Brain: How Body and Environment Shape Animal and Human Minds (Princeton, NJ: Princeton University Press, 2011).
The particular microcontroller we used for Tadros 2 to 4 was a HandyBoard, invented by Fred Martin at MIT (see \en.wikipedia.org/wiki/Handyboard for a great picture and a useful overview). The original Tadro1 had a completely analog electronic brain.
The software that runs on the Handyboard, Interactive C, was originally developed for LEGO Robotics competitions. Two versions of Interactive C are available, one by Newton Labs (www.newtonlabs.com/ic/) and the other by the KISS Institute (www.botball.org/ic).
Alva Noë, Action in Perception (Cambridge, MA: MIT Press, 2004).
For a review of the work of Floreano and his colleagues on this topic, see Mototaka Suzuki and Dario Floreano, “Enactive Robot Vision,” Adaptive Behavior 16, nos. 2–3 (2008): 122–128. Enactive perception has also been put to good use in the training of AMAR-III, a humanoid robot that categorizes objects based on its enactive experience with them (http://spectrum.ieee.org/robotics/artificialintelligence/a-robots-body-of-knowledge).
George Lakoff and Mark Johnson, Philosophy in the Flesh: The Embodied Mind and Its Challenge to Western Thought (New York: Basic Books, 1999).
Ecological psychology was created by J. J. Gibson. Here’s a great place to start: J. J. Gibson, “Visually Controlled Locomotion and Visual Orientation in Animals,” British Journal of Psychology 49, no. 3 (1958): 182–194.
Lawrence W. Barsalou, “Grounded Cognition,” Annual Review of Psychology 59 (2008): 617–645.
Once again I refer you to Pfeifer and Scheier’s excellent book, Understanding Intelligence.
Even if you have heard about Phineas Gage before, you should read this fascinating paper: H. Damasio, T. Grabowski, R. Frank, A. M. Galaburda, and A. R. Damasio, “The Return of Phineas Gage: Clues about the Brain from the Skull of a Famous Patient,” Science 264, no. 5162 (1994): 1102–1105.
NOVA, “Musical Minds,” www.pbs.org/wgbh/nova/musicminds/. A video fMRI of Sack’s brain listening to music can be found at www.pbs.org/wgbh/nova/musicminds/extra.html.
J. M. Fuster, “Upper Processing Stages of the Perception-Action Cycle,” Trends in Cognitive Science 8, no. 4 (2004): 143–145.
E. Tytell, C-Y. Hsui, T. L. Williams, A. V. Cohen, and L. Fauci, “Interactions Between Internal Forces, Body Stiffness and Fluid Environment in a Neuromechanical Model of Lamprey Swimming,” Proceedings of the National Academy of Sciences 107, no. 46 (2010): 19832–19837.
Alan Mathison Turing, “Computing Machinery and Intelligence,” Mind 59, no. 236 (1950): 433–460.
Dedre Gentner and B. Bowdle “Metaphor as Structure-Mapping,” in The Cambridge Handbook of Metaphor and Thought, edited by Raymond W. Gibbs Jr., 109–128 (New York: Cambridge University Press, 2008).
David Kelley, The Art of Reasoning, 3rd ed. (New York: W. W. Norton & Company, 1998).
A good introduction to functionalism and other issues in the philosophy of mind can be found in K. T. Maslin, An Introduction to the Philosophy of Mind, 2nd ed. (Malden, MA: Polity Press, 2007).
Many different flavors of functionalism exist. “Artificial Intelligence” functionalism, for example, attends to the creation of the same kind of intelligence in vertebrates, computers, and robots. I think you can also make a case for what I call Biological Functionalism: when independent evolutionary events converge on similar functional designs. For example, the brains of birds and mammals are very different in terms of how the different parts have evolved compared to their hypothetical common ancestor. But some birds, like crows and parrots, manage to have brains that allow them to make and use tools and language. These convergent abilities show that some birds and mammals possess the same kind of intelligence (= similar function) that is created by different structures. For a great review of the functional similarities and structural differences of birds and mammals, I recommend the following paper: Ann B. Butler and Rodney M. J. Cotterill, “Mammalian and Avian Neuroanatomy and the Question of Consciousness in Birds” Biological Bulletin 211, no. 2 (2006): 106–127.
Robert M. Pirsig, Zen and the Art of Motorcycle Maintenance: An Inquiry into Values (New York: William Morrow, 1974).
Yes, this is the same conceptual cat that Norbert Weiner used, as we spoke of in Chapter 1, to assert problems with modeling: the best model of a cat is a cat. Interesting, is it not, that Weiner’s cat surfaces here as a warning about studying the cat itself? The cat has Weiner’s tongue.
Robert C. Brusca and Gary J. Brusca, Invertebrates, 2nd ed. (Sunderland, MA: Sinauer Associates, 2003).
Georg F. Striedter, Principles of Brain Evolution (Sunderland, MA: Sinauer Associates, 2005).
Howard Gardner, Intelligence Reframed: Multiple Intelligences for the 21st Century (New York: Basic Books, 1999).
This is why, by the way, we do spend time on neuroscience in Introduction to Cognitive Science (Cogs 100)! We also spend time on philosophy because of the important attempts to rationally define and understand what in the world we are trying to talk about when we use words and concepts such as mind, brain, behavior, and intelligence.
A great place to start is with this book: Patricia Churchland, Brain-Wise: Studies in Neurophilosophy (Cambridge, MA: MIT Press, 2002).
If you care about building intelligent machines, you must read this book: Jeff Hawkins and Sandra Blakeslee, On Intelligence: How a New Understanding of the Brain Will Lead to the Creation of Truly Intelligent Machines (New York: Times Books, 2004).
Steven Vogel and Stephen A. Wainwright, A Functional Bestiary: Laboratory Studies about Living Systems. (Reading, MA: Addison-Wesley, 1969), 93.
Descartes is often considered to be the father of cognitive science because he approached the mind-body problem rationally and scientifically. Even though substance dualism was quickly, even in his day, refuted as a scientific theory, we talk about it in cognitive science because it underwrites so much of our intuition about minds, souls, ghosts, and heaven. For an introduction to dualism, visit the Stanford Encyclopedia of Philosophy: \plato.stanford.edu/entries/dualism/#SubDua, or read chapters 1 and 2 in Maslin, An Introduction to the Philosophy of Mind.
This paper does a great job explaining neural circuits and their functions: D. W. Tank, “What Details of Neural Circuits Matter?” Seminars in the Neurosciences 1 (1989): 67–79.
Talk here of circuits and what’s necessary and sufficient to show causal relations of neural circuits to behavior are largely drawn from his book, which I strongly recommend: Thomas J. Carew, Behavioral Neurobiology: The Cellular Organization of Natural Behavior (Sunderland, MA: Sinaur and Associates, 2000).
Carew, again. Ibid.
I really hate the use of the word “simple” in a context like this because it comes packed with so much anthropocentric baggage. One item in our luggage is that we humans assume that we are the most “complex” organisms by any measure. But consider a single-celled organism: in one cell it packs all the basic functions—like eating, moving, and reproducing—that we humans need a multicellular body to perform. As we go on, you’ll see that I call Tadro3 “simple” with specific reference to its sensory-motor system. That’s okay, I’d argue, because I’m being explicit about the system of comparison. Implicit “simplicity” means a thousand different, unspoken things.
Lakoff and Johnson, Philosophy in the Flesh.
George Lakoff, “The Neural Theory of Metaphor,” in Gibbs, The Cambridge Handbook of Metaphor and Thought, 17–38.
Louise Barrett, Beyond the Brain: How Body and Environment Shape Human Minds (Princeton, NJ: Princeton University Press, 2011).
Neurocomputational modeling of swimming vertebrates by Örjan Ekeberg and then Auke Ijspeert have shown that many, many possible circuit structures will produce the same function (functionalism rules!). Thus, we shouldn’t take the two T3 circuits here as the only ones that are possible. Örjan Ekeberg, “A Combined Neuronal and Mechanical Model of Fish Swimming,” Biological Cybernetics 69, nos. 5–6 (1993): 363–374. Auke Jan Ijspeert, John Hallam, and David Willshaw, “Evolving Swimming Controllers for a Simulated Lamprey with Inspiration from Neurobiology,” Adaptive Behavior 7, pt. 2 (1999): 151–172.
Valentino Braitenberg, Vehicles: Experiments in Synthetic Psychology (Cambridge, MA: MIT Press, 1984), 20.
Ibid.
Brooks chronicles this revolution: Rodney A. Brooks, Flesh and Machines: How Robots Will Change Us (New York: Pantheon Books, 2002).
For more on Brooks’s Ghenghis, including its ancestors and descendants, dig around at the website of the MIT Computer Science and Artificial Intelligence Laboratory: www.csail.mit.edu/.
Behavior-based robotics, as a field, was codified by Professor Ronald Arkin in his seminal textbook, Behavior-Based Robotics (Cambridge, MA: MIT Press, 1998). Behavior-based robotics is now recognized as one of the first successful forays into the general field of biologically inspired artificial intelligence. For more, see Dario Floreano and Claudio Mattiussi, Bio-Inspired Artificial Intelligence: Theories, Methods, and Technologies. (Cambridge, MA: MIT Press, 2008).
Rodney Brooks, “A Robust Layered Control System for a Mobile Robot,” A.I. Memo 864, MIT Artificial Intelligence Laboratory, 1985. Published as R. Brooks, “A Robust Layered Control System for a Mobile Robot,” IEEE Journal of Robotics and Automation 2, no. 1 (1986): 14–23.
Matt McHenry, whom you may remember as one of the inventors of Tadro1, and his PhD student, William Stewart, combined experiments on and models of zebrafish to look at the possible influence of a predator on flow around a prey: W. J. Stewart, and M. J. McHenry, “Sensing the Strike of a Predatory Fish Depends on the Specific Gravity of a Prey Fish,” The Journal of Experimental Biology 213, pt. 22 (November 2010): 3769–3777.
For a review of fast starts in the context of predatory-prey situations, read: P. Domenici, “Scaling of Locomotor Performance in Predatory-Prey Encounters: From Fish to Killer Whales,” Comparative Biochemistry and Physiology Part A 131 (2001): 169–182.
This figure of 3 G during takeoff is from mission specialist Koichi Wakata and can be found at the NASA website: \spaceflight.nasa.gov/feedback/expert/answer/crew/sts-92/index.html.
For a lovely review of this fast-start escape circuit, see the following papers: S. J. Zottoli and D. S. Faber, “The Mauthner Cell: What Has It Taught Us?” The Neuroscientist 6, no. 1 (2000): 26–38; R. C. Eaton, R. K. K. Lee, and M. B. Foreman, “The Mauthner Cell and Other Identified Neurons of the Brainstem Escape Response Network,” Progress in Neurobiology 63 (2001): 467–485. Also, excellent experiments on the variety of ways that fish use their reticulospinal circuits for escape and predation have been conducted by Melina Hale, associate professor of organismal biology and anatomy, University of Chicago.
My favorite paper of Fetcho’s on the control of swimming behaviors is K. R. Svoboda and J. R. Fetcho, “Interactions Between the Neural Networks for Escape and Swimming in Goldfish,” Journal of Neuroscience 16, no. 2 (1996): 843–852.
From an interview by New York Times reporter Adam Bryant, “Don’t Lose That Start-Up State of Mind,” October 16, 2010, http://www.nytimes.com/2010/10/17/business/17corner.html?_r=1&ref=adam_bryant.
I encourage you to explore Full’s excellent website: http://polypedal.berkeley.edu/cgi-bin/twiki/view/PolyPEDAL/WebHome.
A reference to Ferengi first officer Kazako from “The Battle,” an episode of Star Trek: The Next Generation (first season).
I’ve tried to address this line of criticism in more detail by placing Tadro and the Tadro evolutionary system in the explicit context of Webb’s system: J. H. Long, “Biomimetic Robotics: Building Autonomous, Physical Models to Test Biological Hypotheses,” Proceedings of the Institution of Mechanical Engineers, Part C, Journal of Mechanical Engineering Science 221 (2007): 1193–1200.
Strictly speaking, Rob’s observation is a logical inference based on the following reasoning: post hoc ergo proctor hoc (after this therefore because of this). In this case, because the three paired sense organs evolved before vertebrae did, then the evolution of vertebrae must be contingent upon those sense organs and the functional capacities that they bring to the vertebrates. We can test this assertion using the new Tadro4 system. Without such a test, by the way, the assertion does not stand on its own accord because many other changes in vertebrates occurred along with the changes in the sense organs.
An open-source physics engine that may be used for simulation of rigid-body dynamics is ODE: www.ode.org/. Unfortunately, this physics engine along with most others does not model the interactions of flexible bodies and fluids. That’s because the physics are very complicated. Thus, we’ve been building and modifying our own physics engine.
If you are interested in Tom’s approach, try: Thomas Ellman, Ryan Deak, and Jason Fotinatos, “Automated Synthesis of Numerical Programs for Simulation of Rigid Mechanical Systems in Physics-Based Animation,” Automated Software Engineering 10, no. 4 (2003): 367–398.
The traits that the engineer allows to vary, just like we allowed traits to vary in our Tadro3, defines the design space. The hill, then, is the combination of those traits that gives the best performance compared to other combinations. Engineers use genetic algorithms instead of doing exhaustive searches of all possible combinations of traits; if you have many traits or features you need to consider, using a genetic algorithm can help you find the hill faster because you don’t rely on your intuition to find the optimum (as defined by a position in multiple dimensions, e.g., the optimal design has a specific weight, drag coefficient, and gear ratio).
You can find the details of the digi-Tad3 simulation in this paper: J. H. Long Jr., M. E. Porter, C. W. Liew, and R. G. Root, “Go Reconfigure: How Fish Change Shape as They Swim and Evolve,” Integrative and Comparative Biology 50, no. 6 (2010): 1120–1139.
This is a line spoken by Miss Vance, played by Katherine Hepburn in the film Bringing Up Baby, as she walked awkwardly following the loss of just one of her high-heeled shoes. I do not mean to imply that Tadro3 limped or wore high heels.
Spoken upon the airborne release of an egg trapped in its container by extraterrestrial Mork, played by Robin Williams, in the television sitcom Mork and Mindy.
By the way, we are always trying to find ways to automate video analysis. I’ll spare you the details, but suffice it to say that we run into three kinds of problems with feature-tracking algorithms: (1) false positives from the light reflected off of the water’s surface, (2) abrupt contrast changes as the Tadros pass into and out of regions of high light intensity, and (3) needing to double-check every automatically processed frame for errors. The closest we’ve come to a nifty solution is to have each Tadro emit an ultrasonic signal that an array of fixed receivers then reads. We simply ran out of money to make this work.
For rules, regulations, and results, see www.worldsolarchallenge.org/.
Because wobble and speed were correlated, as we saw in Chapter 4, it didn’t make sense to use that pair. Also, we did not keep all of the four feeding metrics and then add new predator-avoidance ones on top because we wanted to keep low the number of metrics in order to help us interpret after all was said and done. Finally, average distance from the light is arguably the closest behavioral metric we have to actual light harvesting.
This quote is attributed to David Farragut, naval commander during the US Civil War. Although historians question its veracity, it is, no matter its origin, a damn good quote.
Interestingly, isolation also works in the same way for evolution. It’s on isolated islands, like the Galapagos or the Hawaiian archipelago, that we see rapid evolutionary changes. For a fantastic introduction to rapid evolution on islands and evolutionary processes in general, I recommend this book, mentioned earlier: Jonathan Weiner, The Beak of the Finch: A Story of Evolution in Our Time (New York: Alfred A. Knopf, 1994).
In fishes the vertebral column is seen to have two main sections, the precaudal and the caudal. The precaudal section is what we haughty mammals might be tempted to call “abdominal” because each vertebra is associated with ribs and an underlying visceral cavity. The caudal section comes after (posterior) the precaudal and, in bony fishes, stops at the caudal fin. In sharks, skates, and rays, however, the vertebral column continues all the way up to the tip of the upper lobe of the caudal fin. Thus, in these cartilaginous fishes it’s not clear where the “caudal” vertebrae stop—at the anterior or posterior margin of the caudal fin? Although I’m sure that this is a fascinating topic for most of you, we have to stop. When I say “caudal” here I mean from the last precaudal vertebra to the anterior margin of the caudal fin.
Kurt and Keon presented our work on biomimetic vertebral columns at the Annual Meeting of the Society for Integrative and Comparative Biology: K. Bantilan, K. Combie, J. Schaeffer, D. Pringle, J. H. Long Jr., and T. Koob, “Building Biomimetic Backbones: Modeling Axial Skeleton Morphospace,” Integrative and Comparative Biology 46, suppl. 1 (2006): e8.
From the Metallica song, “Enter Sandman” on the album Metallica. Rock on!
For a detailed comparison of model 3 and model 1 versions, see the following paper: J. H. Long Jr., T. Koob, J. Schaefer, A. Summers, K. Bantilan, S. Grotmol, and M. E. Porter, “Inspired by Sharks: A Biomimetic Skeleton for the Flapping, Propulsive Tail of an Aquatic Robot,” Marine Technology Society Journal 45, no. 4 (2011): 119–129.
The presence of a lateral line is debated, but examination of the different specimens of Drepanaspis shows small canals that may have housed neuromast cells. See D. K. Elliot and E. Mark-Kurik, “A Review of the Lateral Line Sensory System in Psammosteid Heterostracans,” Revista Brasileira de Paleontologia 8, no. 2 (2005): 99–108.
C. E. Brett and S. E. Walker, “Predators and Predation in Paleozoic Marine Environments,” in The Fossil Record of Predation, edited by M. Kowalewski and P. H. Kelley, Paleontological Society Special Papers 8 (2002): 93–118.
Westneat, an expert on the biomechanics of fish feeding, produced this jaw-dropping analysis: P. S. L. Anderson and M. W. Westneat, “Feeding Mechanics and Bite Force Modeling of the Skull of Dunkleosteus terrelli, an Ancient Apex Predator,” Biology Letters 3, no. 1 (2007): 77–80.
An important point to keep in mind here, if you want to try this system in your own Evolvabot, is that because Tadros are surface swimmers, the IR sensors mounted above the water line are working in air.
To beat a dead horse, as the saying goes, keep in mind that correlated evolution can turn out to be either concerted or mosaic. In spite of this flogging, you shouldn’t be deterred from investigating this character evolution approach. Here’s a great place to start: Michael I. Coates and Martin J. Cohn, “Developmental and Evolutionary Perspectives on Major Transformation in Body Organization—Vertebrate Axial and Appendicular Patterning: The Early Development of Paired Appendages,” American Zoologist 39, no. 3 (1999): 676–685. Also: Robert A. Barton and Paul H. Harvey, “Mosaic Evolution of Brain Structure in Mammals,” Nature 405, no. 6790 (2000): 1055–1058.
D.-G. Shu, S. Conway Morris, J. Han, Z.-F. Zhang, K. Yasui, P. Janvier, L. Chen, X.-L. Zhang, J.-N. Liu, Y. Li, and H.-Q. Lui, “Head and Backbone of the Early Cambrian Vertebrate Haikouichthys,” Nature 421, no. 6922 (2003): 526–529.
The term “Kutta condition” describes the physical situation at the trailing edge that is used in the Runge-Kutta theorem of inviscid flow. Runge-Kutta is used to estimate the pattern of flow around a body, including the so-called separation and stagnation points. The pattern of flow around a fish’s body is constantly changing as the fish undulates its body, and the caudal fin is the place where water that has interacted with the body is shed rearward, creating a wake. The wake, in turn, can be thought of as evidence of the momentum that the fish has transferred from its body in order to move forward, thanks to Newton’s third law.
When they started the predator-prey trials for generation six, the students noticed a change in the behavior of the robots that turned out to be related to the servo motors breaking down. The wisdom of their observations had been informed, in part, by the fact that at the start of every generation they ran positive controls on PreyRo and Tadiator, using a fixed “control tail” to assess any degradation in the hardware. Although we normally keep spare and identical parts on hand for just such a breakdown, we were fresh out of servos and so was the supplier. This pause in evolutionary activities allowed us to analyze this first run, which we published: N. Doorly, K. Irving, G. McArthur, K. Combie, V. Engel, H. Sakhtah, E. Stickles, H. Rosenblum, A. Gutierrez, R. Root, C.-W. Liew, and J. H. Long Jr., “Biomimetic Evolutionary Analysis: Robotically-Simulated Vertebrates in a Predator-Prey Ecology,” Proceedings of the 2009 IEEE Symposium on Artificial Life (2009): 147–154.
Spoken by Don Lockwood, in the movie Singing in the Rain, as he attempts to recast his past dowdy Vaudeville career in the color of his current Hollywood persona.
I’m playing a bit fast and loose with my use of “concerted” here, so please help me out by keeping in mind that a correlation is only the first piece of evidence for concerted evolution. Because we’ve defined concerted evolution to be causally based, we also want to show how, in this case, the number of vertebrae improves the acceleration ability of the PreyRo. I’ll show you evidence for this in some experiments that we run on the Tadro-derived MARMT system in the next chapter.
This opening line is from the movie, The Adventures of Buckaroo Bonzai Across the Eighth Dimension, 1984.
Even though we spoke at length in Chapter 2 about evolutionary theory, you may hunger for more (or for a refresher). Consider starting at this website, “Understanding Evolution,” \evolution.berkeley.edu/evolibrary/home.php.
You may recognize this problem from our discussion of scientific inference in Chapter 2. We only ever see a limited number of cases of all of the phenomena we seek to understand. Once we infer some property of all possible cases from witnessing just a few, we are always worried, with good reason, about the other cases. What if one of those unseen cases falsifies my idea of how the system is working? With that very real concern in mind, we conduct additional tests, make sure that we sampled the study cases within the system in a way that best represents all of the possible cases, and “prove” by being unable to disprove after repeated attempts to do so.
Some folks argue that this question is the motivation for most of evolutionary biology. It was addressed by Sewall Wright, who, in the first part of the twentieth century, extended individual genetics to the genetics of populations. In so doing, he helped propel the modern synthesis of evolutionary theory, which includes his concept of an “adaptive landscape,” wherein a population has an evolutionary path that is determined by, you guessed it, history, selection, and random genetic effects. Check out his paper: Sewall Wright, “The Roles of Mutation, Inbreeding, Crossbreeding and Selection in Evolution,” Proceedings of the Sixth International Congress of Genetics 1 (1932): 356–366. The theory of adaptive landscapes, though modified, is currently used to study, for example, the pathways of molecular evolution: F. J. Poelwijk, D. J. Kiviet, D. M. Weinreich, and S. J. Tans, “Empirical Fitness Landscapes Reveal Accessible Evolutionary Paths,” Nature 445, no. 7126 (2007): 383–386.
This question springs from Steven J. Gould’s point about the importance of historical contingency. He argues that chance events in life make it highly unlikely that any species would evolve along the same path given a second opportunity to do so. Steven J. Gould, Wonderful Life: The Burgess Shale and the Nature of History (New York: W. W. Norton, 1989). Others argue that chance plays less of a role and that some forms have a high-probability of re-evolving. The genetics of developmental systems may constrain those possibilities. For a great introduction to that subject, read Sean B. Carroll, Endless Forms Most Beautiful: The New Science of Evo Devo and the Making of the Animal Kingdom (New York: W. W. Norton, 2005).
This question is a variant on these: Why is morphospace clumped? Why is biodiversity limited? What kinds of life-forms are physically possible? Genetically possible?
We calculate the selection vector as follows. First, the fitness scores determine who gets to mate. For PreyRo, the top three out of six get to mate, with the first-, second-, and third-place winners contributing to six, four, and two gametes, respectively, to the mating pool. Second, before we mutate those gametes or allow them to join to make offspring, we calculate the average values of the traits of the pre-mutation and pre-mating offspring. Third, this average of the traits is the position of the head of the selection vector’s arrow, with the vector’s tail anchored at the average of the parental population.
Warning: I made the peaks on this map in an intuitive and qualitative manner. In other words, I guessed. Well, it’s a bit better than guesswork, but not much, given how little data we’ve got here. Knowing that the selection vectors point uphill, I knew where at least some peaks or ridges needed to be. The guesswork comes in as follows. For the selection vectors from generations one and two, I assumed that they were pointing to a ridge. I could have assumed that they pointed to two separate peaks. I didn’t, though, because the direction that they point is similar, south-south-east for generation one and south-east for generation two, and I took that to mean that they were pointing at the same adaptive structure. This guesswork shows you how much data you would need to create a comprehensive adaptive landscape.
C. W. Liew and M. Lahiri, “Exploration or Convergence? Another Meta-Control Mechanism for GAs,” in Proceedings of the 18th International Florida Artificial Intelligence Research Society Conference, 251–257 (Clearwater Beach, FL: AAAI Press, 2005).
“Phenomenal cosmic power! Itty-bitty living space!” said the genie in Aladdin, the 1992 Disney film.
Thanks to Charles Dickens.
General Kurtz in Francis Ford Coppola’s 1979 film Apocalypse Now.
Barbara Webb, “Can Robots Make Good Models of Behaviour?” Behavioral and Brain Sciences 24, no. 6 (2001): 1048.
Ibid., 1049.
Marcel Proust, À la recherche du temps perdu, translated by C. K. Scott Moncreiff and Terence Kilmartin as Remembrance of Things Past (New York: Vintage Books, 1982).
Christopher McGowan, The Dragon Seekers: How an Extraordinary Circle of Fossilists Discovered the Dinosaurs and Paved the Way for Darwin (New York: Basic Books, 2001).
H. T. de la Beche and W. D. Conybeare, “Notice of the Discovery of a New Animal, Forming a Link Between the Ichthyosauru s and Crocodile, Together with General Remarks on the Osteology of Ichthyosaurus,” Transactions Geological Society London 5 (1821): 559–594.
Richard Forrest has created and maintains an excellent site on plesiosaurs that you should visit: \plesiosaur.com/. Dr. Adam Stuart Smith also has an excellent site that features his own research: www.plesiosauria.com/index.html.
The term “plesiosaur” can be confusing. For example, within the Order Plesiosauria, we’ve got the short-necked Pliosauroidea and the long-necked Plesiosauroidea as Suborders. When I use the term plesiosaur here, I include all members of the Order, after Adam Stuart Smith (www.plesiosauria.com/classification.html). Just keep in mind that some folks prefer to talk about “true plesiosaurs” as just the long-necked forms, leaving pliosaurs to the side.
Carl Zimmer, At the Water’s Edge: Fish with Fingers, Whales with Legs, and How Life Came Ashore but Then Went Back to Sea (New York: Touchstone, 1998).
J. Lindgren, M. W. Caldwell, T. Konishi, and L. M. Chiappe, “Convergent Evolution in Aquatic Tetrapods: Insights from an Exceptional Fossil Mosasaur,” PLoS One 5, no. 8 (2010): e11998, doi:10.1371/journal.pone.0011998.
Start here with two of Frank’s papers: “Transitions from Drag-Based to Lift-Based Propulsion in Mammalian Aquatic Swimming,” American Zoologist 36, no. 5 (1996): 628–641, and “Biomechanical Perspective on the Origin of Cetacean Flukes,” in The Emergence of Whales: Evolutionary Patterns in the Origin of Cetacea, edited by J. G. M. Thewissen, 303–324 (New York: Plenum Press, 1998).
These flippers, or Nektors, are themselves biologically inspired. Charles Pell, working with a graduate student at Duke University in the BioDesign Studio that he and Professor Steve Wainwright created, noticed that a fish-like piece of rubber, mounted on a stick, would generate thrust if you wiggled the stick between your fingers, rolling the stick between thumb and forefinger, with the fish in the water. Pell, then-student-of-mine Matt McHenry, and I used Nektors as model representations of blue-gill sunfish to analyze swimming propulsion: M. J. McHenry, C. A. Pell, and J. H. Long Jr., “Mechanical Control of Swimming Speed: Stiffness and Axial Wave Form in an Undulatory Fish Model,” Journal of Experimental Biology 198 (1995): 2293–2305. Pell and Wainwright patented the Nektor system: C. A. Pell, and S. A. Wainwright, “Swimming Aquatic Creature Simulator,” US Patent 6179683, issued January 30, 2001, assigned to Nekton Technologies, Inc. (now the marine division of iRobot, Inc.).
Tellingly, petit madeleines are modeled after scallops! If you buy madeleine pans, you’ll notice right away the fluted and streamlined depressions into which you pour the batter. What’s cool about scallops is that they are bivalves, mollusks with two shells, that actually swim. So here we have a swimming scallop that is the model for a streamlined pastry that is the inspiration of the name of a swimming and streamlined biorobot. Does it get any more fun?
Forgive the engineer-speak about to issue forth. Maddie’s flippers, or Nektors, are single-degree-of-freedom actuators. A shaft colinear with a rotary motor moves the flipper, the compliant material forming the shape and bulk of the appendage, which is molded around the shaft at a specified angular velocity. The flipper is oriented so that its leading edge rotates in pitch. That pitch rotation flaps the flipper and transfers angular momentum to the surrounding water. When the pitch rotation is reciprocated such that the direction of the angular velocity alters regularly, as with a sine function, then the momentum transferred from the flipper to the water can be focused as a jet. This jet, in turn, produces a net thrust, via Newton’s third law, on the oscillating flipper.
The coaches of Vassar’s swim teams, Lisl Prater-Lee, Tom Albright, and Jesup Szatkowski, were kind enough to allow Robot Madeleine both training and experiment time in the pool.
You can find all of the details of this set of experiments in the following paper: J. H. Long Jr., J. Schumacher, N. Livingston, and M. Kemp, “Four Flippers or Two? Tetrapodal Swimming with an Aquatic Robot,” Bioinspiration & Biomimetics (Institute of Physics) 1 (2006): 20–29. We first introduced Robot Madeleine here: M. Kemp, B. Hobson, and J. H. Long Jr., “Madeleine: An Agile AUV Propelled by Flexible Fins,” in Proceedings of the 14th International Symposium on Unmanned Untethered Submersible Technology (UUST), Autonomous Undersea Systems Institute, Lee, NH, 2005.
F. E. Fish, J. Hurle, and D. P. Costa, “Maneuverability by the Sea Lion Zalophus californianus: Turning Performance of an Unstable Body Design,” The Journal of Experimental Biology 206, pt. 4 (February 2003): 667–674.
Predator X is the stage name of a heretofore undescribed species of pliosaur unearthed in the Norwegian Arctic. The History Channel aired an eponymous special on Predator X, and clips of the documentary are available at www.history.com/videos/predator-x-revealed#predator-x-revealed. Robot Madeleine, by the way, was featured!
The accelerations of twenty-two-meter-to twenty-seven-meter-long blue whales have been measured in the wild: J. A. Goldbogen, J. Calambodkidis, E. Oleson, J. Potvin, N. D. Pyenson, G. Schorr, and R. E. Shadwick, “Mechanics, Hydrodynamics and Energetics of Blue Whale Lunge Feeding: Efficiency Dependence on Krill Density,” The Journal of Experimental Biology 214, no. 1 (2011): 131–146.
Robot Madeleine, like Tadro, has had multiple versions. Maddie 1.0 was self-propelled and controlled remotely by a human operator. Maddie 2.0 had all the on-board sensors, like the power monitor and the accelerometer, allowing her to collect data on herself. Maddie 2.0 was the version that I’ve talked about here and about which we’ve published our papers. Maddie 3.0 was programmed by Mathieu Kemp to be fully autonomous, employing a two-layer subsumption hierarchy (see Chapter 5) in which she selected a random depth and compass heading and then moved along that course until she either detected an object with her sonar or ran out of time (thirty seconds). Maddie 3.0 was destroyed, unfortunately, when we were filming her for the documentary Predator X; she sprung a leak and fried her electronics. Since then we have been trying to rebuild her as Maddie 4.0 at Vassar; however, at the moment we lack the funding to finish that job.
You can see Transphibians at the iRobot website: www.irobot.com/gi/maritime/Transphibian/.
B. W. Hobson, M. Kemp, R. Moody, C. A. Pell, and F. Vosburgh, “Amphibious Robot Devices and Related Methods,” US Patent 6,974,356, 2005.
Broadcast date of August 9, 2006.
Auke Jan Ijspeert, Alessandro Crespi, Dimitri Ryczko, and Jean-Marie Cabelguen, “From Swimming to Walking: Is a Salamander Robot Driven by a Spinal Cord Model?” Science 315, no. 5817 (2007): 1416–1420.
You can read more about MARMT in J. H. Long Jr., N. Krenitsky, S. Roberts, J. Hirokawa, J. de Leeuw, and M. E. Porter, “Testing Biomimetic Structures in Bioinspired Robots: How Vertebrae Control the Stiffness of the Body and the Behavior of Fish-like Swimmers,” Integrative and Comparative Biology 51, no. 1 (2011): 158–175, doi:10.1093/icb/icr020.
Where would we be without Douglas Adams? This chapter title is an homage to the fourth book in his Hitchhiker’s Guide to the Galaxy series, So Long, and Thanks for All the Fish (New York: Harmony Books, 1985).
Here’s MHI’s original press release: www.mhi.co.jp/en/news/sec1/e_0898.html.
You can learn more about this company’s plans at www.robotswim.com.
Reported by the Huffington Post, July 16, 2010, based on a Reuters video posted July 15, 2010. Or, better yet, visit Dr. Porfiri’s web page for the real scoop: \faculty.poly.edu/~mporfiri/index.htm.
Full disclosure here: I have been and currently am collaborating with Farshad and FarCo Technologies. However, I hold no financial stake in FarCo Technologies (www.farcotech.com/).
P. R. Bandyopadhyay, “Swimming and Flying in Nature—The Route Toward Applications: The Freeman Scholar Lecture,” Journal of Fluids Engineering 131, no. 3 (March 2009): 0318011–0318029.
Steven Vogel, Cats’ Paws and Catapults: Mechanical Worlds of Nature and People (New York: W. W. Norton, 1998), 10.
E-mail message from Melina Hale, January 7, 2011.
Full disclosure: I am hired by the European Commission as an outside expert evaluator of the FILOSE project, which the EC funds as part of their Seventh Framework Programme.
For the latest on FILOSE Fish, see this paper: M. Kruusmaa, T. Salumae, G. Toming, A. Ernits, and J. Ježov, “Swimming Speed Control and On-board Flow Sensing of an Artificial Trout,” Proceedings of the IEEE International Conference of Robotics and Automation (IEEE ICRA 2011), Shanghai, China, May 9–13, 2011.
See the full statement at this URL: cordis.europa.eu/fp7/understand_en.html.
Work on the fish and the biomimetic robot is explained in: O. M. Curet, N. A. Patankar, G. V. Lauder, and M. A. MacIver, “Aquatic Manoeuvering with Counter-Propagating Waves: A Novel Locomotive Strategy,” Journal of the Royal Society Interface 8, no. 60 (July 2011), 1041–1050, doi:10.1098/rsif.2010.0493.
For more on their robotic fish fin, see: Chris Phelan, James Tangorra, George Lauder, and Melina Hale, “A Biorobotic Model of the Sunfish Pectoral Fin for Investigations of Fin Sensorimotor Control,” Bioinspiration & Biomimetics 5, no. 3 (2010); James Louis Tangorra, S. Naomi Davidson, Ian W. Hunter, Peter G. A. Madden, George V. Lauder, Dong Haibo, Meliha Bozkurttas, and Rajat Mittal, “The Development of a Biologically Inspired Propulsor for Unmanned Underwater Vehicles,” IEEE Journal of Oceanic Engineering 32, no. 3 (2007): 533–550.
If you are interested in other fish-inspired robots, I review the field in “Biomimetics: Robotics Based on Fish Swimming,” in Encyclopedia of Fish Physiology: From Genome to Environment, vol. 1, edited by A. P. Farrell, 603–612 (San Diego: Academic Press, 2011).
Conversation at the Annual Meeting of the Society for Integrative and Comparative Biology, January 4, 2011.
I use the date of 1946 here because that was when President Truman created the ONR to “plan, foster and encourage scientific research in recognition of its paramount importance as related to the maintenance of future naval power, and the preservation of national security.” The Navy, however, considers ONR to have been started earlier, in 1923, as the Naval Research Laboratory. See their timeline at www.onr.navy.mil/About-ONR/History-ONR-Timeline.aspx.
In vibratory mechanics the natural frequency of a structure is proportional to the square root of its stiffness. Other factors, like mass and damping, shouldn’t be neglected because they play huge roles in how the structure moves.
Details of the experiments that originally led us to this prediction can be found in the following paper: J. H. Long Jr., M. J. McHenry, and N. C. Boetticher, “Undulatory Swimming: How Traveling Waves Are Produced and Modulated in Sunfish (Lepomis gibbosus),” Journal of Experimental Biology 192 (1994): 129–145.
You can read more about these early robotic fish in the following paper: M. J. McHenry, C. A. Pell, and J. H. Long Jr. “Mechanical Control of Swimming Speed: Stiffness and Axial Wave Form in an Undulatory Fish Model,” Journal of Experimental Biology 198 (1995): 2293–2305.
For a summary of the ONR’s biorobotics program through 2005, see P. R. Bandyopadhya, “Trends in Biorobotic Autonomous Undersea Vehicles,” IEEE Journal of Oceanic Engineering 30, no. 1 (2005): 109–139.
DARPA’s mission statement can be found at www.darpa.mil/mission.html.
For more on the design and performance of RiSE, see M. J. Spenko, G. C. Haynes, J. A. Saunders, M. R. Cutkosky, A. A. Rizzi, R. J. Full, and D. E. Koditschek, “Biologically Inspired Climbing with a Hexapedal Robot,” Journal of Field Robotics 25, no. 4 (2008): 223–242.
For example, see DARPA CBS-ONR-ARL US Navy Marine Mammal Program, Biosonar Program Office, SPAWAR Systems Center, San Diego, CA, 2002. See also Frank E. Fish, “Review of Natural Underwater Modes of Propulsion,” DARPA, 2000. More recent projects include bio-inspired underwater sensing and autonomous underwater navigation in rivers and estuaries. For more on the workings of DARPA, I recommend Michael Belfiore, The Department of Mad Scientists: How DARPA Is Remaking our World, from the Internet to Artificial Limbs (Washington, DC: Smithsonian Books, 2009).
I checked DARPA’s public solicitation on January 8, 2011, at www.darpa.mil/openclosedsolicitations.html.
As reported by John Markoff, “War Machines: Recruiting Robots for Combat,” New York Times, November 27, 2010.
Professor Arkin’s book is timely and opens up an important discussion: Ronald C. Arkin, Governing Lethal Behavior in Autonomous Robots (Boca Raton, FL: Chapman & Hall/CRC, 2009).
Nowadays, the US Coast Guard has eleven missions: www.uscg.mil/top/missions/.
This is the translation given by Gilbert in his comprehensive book: Martin Gilbert, The First World War: A Complete History (New York: Henry Holt, 1994), 352. Horace’s phrase has other translations, including, “It is sweet and right to die for your country.”
This is an excerpt of Owen’s “Dulce et Decorum Est,” which can be found in full and with notes at the War Poetry website: www.warpoetry.co.uk/owen1.html.
Michael Herr, Dispatches (New York: Alfred A. Knopf, 1977).
Peter and Craig’s model can be found in this article: P. J. Czuwala, C. Blanchette, S. Varga, R. G. Root, and J. H. Long Jr., “A Mechanical Model for the Rapid Body Flexures of Fast-Starting Fish,” in Proceedings of the 11th International Symposium on Unmanned Untethered Submersible Technology (UUST), 415–426 (Lee, NH: Autonomous Undersea Systems Institute, 1999). At the same meeting Rob presented this paper: R. G. Root, H-W. Courtland, C. A. Pell, B. Hobson, E. J. Twohig, R. J. Suter, W. R. Shepherd, III, N. Boetticher, and J. H. Long Jr., “Swimming Fish and Fish-like Models: The Harmonic Structure of Undulatory Waves Suggests That Fish Actively Tune Their Bodies,” in Proceedings of the 11th International Symposium on Unmanned Untethered Submersible Technology (UUST), 378–388 (Lee, NH: Autonomous Undersea Systems Institute, 1999).
The irony is that secrecy is enforced when I work with and advise companies. Both business and the military use secrecy to maintain an advantage over the competition or adversaries. For the record, I honor all of my agreements with businesses to keep our proprietary work secret.
The race continues unabated: E. Bumiller and T. Shanker, “War Evolves with Drones, Some Tiny as Bugs,” New York Times, June 19, 2011.
For the latest on autonomous robots in war: L. G. Weiss, “Autonomous Robots in the Fog of War,” IEEE Spectrum 48, no. 8 (2011): 30–57.
Silke Steingrube, Marc Timme, Florentin Worgotter, and Poramate Manoonpong, “Self-Organized Adaptation of a Simple Neural Circuit Enables Complex Robot Behaviour,” Nature Physics 6, no. 3 (2010): 224–230.
Two groundbreaking papers by Lipson that you simply must read: H. Lipson and J. B. Pollack, “Automatic Design and Manufacture of Artificial Lifeforms,” Nature 406, no. 6799 (2000): 974–978; and J. Bongard, V. Zykov, and H. Lipson, “Resilient Machines Through Continuous Self-Modeling,” Science 314, no. 5802 (November 2006): 1118–1121.
Bongard explains his approach on his web page: www.cs.uvm.edu/~jbongard/research.html.
Penrose the Elder summarized their work in this article: L. S. Penrose, “Self-Reproducing Machines,” Scientific American 200, no. 6 (June 1959): 105–114.
You can watch the Penrose machines replicating in this film, made in 1961: http://vimeo.com/10298933.
MicroHunter was invented by Chuck Pell, Hugh Crenshaw, Jason Janet, and Mathieu Kemp and was assigned to Nekton Technologies, Inc., US Patent 6,378,801. C. Pell, H. Crenshaw, J. Janet, and M. Kemp, “Devices and Methods for Orienting and Steering in Three-Dimensional Space,” 2002. A great place to get an overview of MicroHunter is in J. Wakefield, “Mimicking Mother Nature,” Scientific American 286, no. 1 (January 2002): 26–27.
For more on MicroHunter, see M. Kemp, H. Crenshaw, B. Hobson, J. Janet, R. Moody, C. Pell, H. Pinnix, and B. Schulz, “Micro-AUVs I: Platform Design and Multiagent System Development,” in Proceedings of the 12th International Symposium on Unmanned Untethered Submersible Technology (UUST), 2001.
For more on Navy SEALs, see their website: www.navyseal.com/navy_seal/.
US Army Field Manual (FM) 100–105, Operations (Washington, DC: Government Printing Office [GPO], 1993), 6.
R. H. Kewley and M. J. Embrechts, “Computational Military Tactical Planning System,” IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews 32, no. 2 (2002): 161–171.
L. G. Shattuck, “Communicating Intent and Imparting Presence,” Military Review 80, pt. 2 (March–April 2000): 66–72.
Ronald Arkin, an expert robotics engineer, is the leader in considering both the practical and philosophical aspects of the ethics of using robots in war. His first paper on the subject is a good place to start: “Governing Lethal Behavior: Embedding Ethics in a Hybrid Deliberative/Reactive Robot Architecture—Part 1: Motivation and Philosophy,” Proceedings of Human-Robot Interaction 2008, Amsterdam, Netherlands, 2008.
Ronald Arkin, Governing Lethal Behavior in Autonomous Robots (Boca Raton, FL: Chapman & Hall/CRC, 2009), 2.