The structure of an environment affects the behaviors of the organisms that have evolved in it. How is that structure to be described, and how can its behavioral consequences be explained and predicted? We aim to establish initial answers to these questions by simulating the evolution of very simple organisms in simple environments with different structures. Our artificial creatures, called "minimats, "have neither sensors nor memory and behave solely by picking amongst the actions of moving, eating, reproducing, and sitting, according to an inherited probability distribution. Our simulated environments contain only food (and multiple minimats) and are structured in terms of their spatial and temporal food density and the patchiness with which the food appears. Changes in these environmental parameters affect the evolved behaviors of minimats in different ways, and all three parameters are of importance in describing the minimat world. One of the most useful behavioral strategies that evolves is "looping" movement, which allows minimats-despite their lack of internal state—to match their behavior to the temporal (and spatial) structure of their environment. Ultimately we find that minimats construct their own environments through their individual behaviors, making the study of the impact of global environment structure on individual behavior much more complex.
Ackley, D., & Littman, M. (1992). Interactions between learning and evolution. In C. G. Langton, C. Taylor, J. D. Farmer, & S. Rasmussen (Eds.), Artificial life II (pp. 487-509). Reading, MA: Addison-Wesley.
2.
Ackley, D., & Littman, M. (1994). Altruism in the evolution of communication. In R. A. Brooks & P. Maes (Eds.), Artificial Life IV: Proceedings of the Fourth International Workshop on the Synthesis and Simulation of Living Systems (pp. 40-48). Cambridge, MA: MIT Press/Bradford Books.
3.
Arbib, M.A., & Cobas, A. (1991). Schemas for prey-catching in frog and toad. In J.-A. Meyer & S. W Wilson (Eds.), From animals to animats: Proceediiigs of the First International Conference on Simulation of Adaptive Behavior (pp. 142-151). Cambridge, MA: MIT Press/Bradford Books.
Bedau, M.A. (1996). The extent to which organisms construct their environments. Adaptive Behavior, 4(3-4), 469-475.
6.
Bedau, M.A., & Packard, N.H. (1992). Measurement of evolutionary activity, teleology, and life. In C. G. Langton, C. Taylor, J. D. Farmer, & S. Rasmussen (Eds.), Artificial life II (pp. 431-461). Reading, MA: Addison-Wesley .
7.
Bell, W.J. (1991). Searching behavior: The behavioural ecology of finding resources. London: Chapman and Hall.
8.
Blythe, P.W., Miller, G.F., & Todd, P.M. (1996). Human simulation of adaptive behavior: Interactive studies of pursuit, evasion, courtship, fighting, and play. In P. Maes, M. J. Mataric, J.-A. Meyer, J. Pollack, & S. W Wilson (Eds.), From animals to animats 4: Proceedings of the Fourth International Conference on Simulation of Adaptive Behavior (pp. 13-22). Cambridge, MA : MIT Press/Bradford Books.
9.
Cecconi, F., & Parisi, D. (1993). Neural networks with motivational units. In J.-A. Meyer, H. L. Roitblat, & S. W Wilson (Eds.), From animals to animats 2: Proceedings of the Second International Conference on Simulation of Adaptive Behavior (pp. 346-355). Cambridge, MA: MIT Press/Bradford Books.
10.
Cliff, D., & Bullock, S. (1993). Adding "foveal vision" to Wilson's animat. Adaptive Behavior, 2(1), 49-72.
11.
Cliff, D., Harvey, I., & Husbands, P. (1993). Explorations in evolutionary robotics. Adaptive Behavior, 2(1), 73-110.
12.
Cliff, D., & Ross, S. (1994). Adding temporary memory to ZCS. Adaptive Behavior, 3(2), 101-150.
13.
Collins, R.J., & Jefferson, D.R. (1992). AntFarm: Towards simulated evolution. In C. G. Langton, C. Taylor, J. D. Farmer, & S. Rasmussen (Eds.), Artificial life II (pp. 579-601). Reading, MA: Addison-Wesley .
14.
Erdmann, M.E., & Mason, M.T. (1988). An exploration of sensorless manipulation. IEEE Journal of Robotics and Automation, 4, 369-379.
15.
Fletcher, J.A., Zwick, M., & Bedau, M.A. (1996). Dependence of adaptability on environmental structure in a simple evolutionary model. Adaptive Behaviot; 4(3-4), 275-307.
16.
Floreano, D. (1993a). Emergence of nest-based foraging strategies in ecosystems of neural networks. In J.-A. Meyer , H. L. Roitblat, & S. W Wilson (Eds.), From animals to animats 2: Proceedings of the Second International Conference on Simulation of Adaptive Behavior (pp. 410-416). Cambridge, MA: MIT Press/Bradford Books.
17.
Floreano, D. (1993b, May). Patterns of interactions in shared environments . In Preproceedings of the Second European Conference on Artificial Life, Brussels, Belgium.
18.
Godfrey-Smith, P. (1996). Précis of Complexity and the function of mind in nature. Adaptive Behavior, 4(3-4), 443-455.
19.
Gracias, N., Pereira, H., Lima, J.A., & Rosa, A. (1996, May). Gaia: An artificial life environment for ecological systems simulation. In ALIFE V oral presentations (preproceedings of the Artificial Life V workshop, Nara, Japan, pp. 105-112).
20.
Grant, W. (1992, June). Artificial life worlds as discovery environments for learning. Talk presented at the Artificial Life III Workshop, Santa Fe, NM.
21.
Hartvigsen, G., & Starmer, W.T. (1995). Plant-herbivore coevolution in a spatially and genetically explicit model. Artificial Life, 2(2), 239-259.
22.
Hendriks-Jansen, H. (1996). Catching ourselves in the act: Situated activity, interactive emergence, evolution, and human thoughtCambridge, MA: MIT Press/Bradford Books.
23.
Jefferson, D., Collins, R., Cooper, C., Dyer, M., Flowers, M., Korf, R., Taylor, C., & Wang, A. (1992). Evolution as a theme in artificial life: The Genesys/Tracker system. In C. G. Langton, C. Taylor, J. D. Farmer, & S. Rasmussen (Eds.), Artificial life II (pp. 549-578). Reading, MA: Addison-Wesley .
24.
Kerr, R.A. (1996). New mammal data challenge evolutionary pulse theory . Science, 273, 431-432.
25.
Kirsh, D. (1996). Adapting the environment instead of oneself. Adaptive Behavior, 4(3-4), 405-442.
26.
Koza, J.R. (1992). Genetic evolution and co-evolution of computer programs. In C. G. Langton, C. Taylor , J. D. Farmer , & S. Rasmussen (Eds.), Artificial life II (pp. 603-629). Reading, MA: Addison-Wesley.
27.
Littman, M.L. (1992). An optimization-based categorization of reinforcement learning environments . In J.-A. Meyer, H. L. Roitblat, & S. W Wilson (Eds.), From animals to animats 2: Proceedings of the Second International Conference on Simulation of Adaptive Behavior (pp. 262-270). Cambridge, MA: MIT Press/Bradford Books.
28.
Littman, M.L. (1994). Memoryless policies: Theoretical limitations and practical results. In D. Cliff, P Husbands, J.-A. Meyer, & S. W. Wilson (Eds.), From animals to animats 3: Proceedings of the Third International Conference on Simulation of Adaptive Behavior (pp. 238-245). Cambridge, MA: MIT Press/Bradford Books.
29.
Lund, H.H., & Parisi, D. (1995). Pre-adaptation in populations of neural networks evolving in a changing environment. Artificial Life, 2(2), 179-197.
30.
Lund, H.H., & Parisi, D. (1996, May). Generalist and specialist behavior due to individual energy extracting abilities. In ALIFE V oral presentations (preproceedings of the Artificial Life V workshop, Nara, Japan, pp. 294-304).
Maley, C. (1996, May). Simulating invasions in a spatially structured ecosystem. In ALIFE V poster presentations (preproceedings of the Artificial Life V workshop, Nara, Japan, pp. 205-211).
33.
Mason, M.T. (1993). Kicking the sensing habit. AI Magazine, 1, 58-59.
34.
Menczer, F., & Belew, R.K. (1994). Evolving sensors in environments of controlled complexity. In R. A. Brooks & P Maes (Eds.), Artificial Life IV: Proceedings of the Fourth International Workshop on the Synthesis and Simulation of Living Systems (pp. 210-221). Cambridge, MA: MIT Press/Bradford Books.
35.
Menczer, F., & Belew, R.K. (1996a). Latent energy environments. In R. K. Belew & M. Mitchell (Eds.), Adaptive individuals in evolving populations: Models and algorithms. Reading, MA: Addison-Wesley
36.
Menczer, F., & Belew, R.K. (1996b). From complex environments to complex behaviors . Adaptive Behavior, 4(3-4), 309-355.
37.
Packard, N.H. (1989). Intrinsic adaptation in a simple model for evolution . In C. G. Langton (Ed.), Artificial life (pp. 141-155). Redwood City, CA: Addison-Wesley.
38.
Parisi, D., Nolfi, S., & Cecconi, F. (1992). Learning, behavior, and evolution. In F.J. Varela & P Bourgine (Eds.), Towards a practice of autonomous systems: Proceedings of the First European Conference on Artificial Life (pp. 207-216). Cambridge, MA: MIT Press/Bradford Books.
39.
Resnick, M. (1994). Turtles, termites, and traffic jams: Explorations in massively parallel microworlds. Cambridge, MA: MIT Press/Bradford Books.
40.
Shepard, R.N. (1987). Evolution of a mesh between principles of the mind and regularities of the world. In J. Dupré (Ed.), The latest on the best: Essays on evolution and optimality (pp. 251-275). Cambridge, MA: MIT Press/Bradford Books. Simon, H.A. (1956). Rational choice and the structure of the environment. Psychological Review, 63(2), 129-138.
41.
Smithers, T. (1994). On why better robots make it harder. In D. Cliff, P Husbands, J.-A. Meyer, & S. W. Wilson (Eds.), From animals to animats 3: Proceedings of the Third International Cot ference on Simulation of Adaptive Behavior (pp. 64-72). Cambridge, MA: MIT Press/Bradford Books .
Taylor, C.E., Jefferson, D.R., Turner, S.R., & Goldman, S.R. (1989). RAM: Artificial life for the exploration of complex biological systems. In C. G. Langton (Ed.), Artificial life (pp. 275-295). Redwood City, CA: Addison-Wesley.
44.
Todd, P.M. (1993, May). Artificial death. In Pre-proceedings of the Second European Conference on Artificial Life, Brussels, Belgium.
45.
Todd, P.M., & Miller, G.F. (1991a). Exploring adaptive, agency II: Simulating the evolution of associative learning. In J.-A. Meyer & S. W Wilson (Eds.), From animals to animats: Proceedings of the First International Conference on Simulation of Adaptive Behavior (pp. 306-315). Cambridge, MA: MIT Press/Bradford Books.
46.
Todd, P.M., & Miller, G.E. (1991b). Exploring adaptive agency III: Simulating the evolution of habituation and sensitization. In H.-P. Schwefel & R. Maenner (Eds.), Proceedings of the First International Conference on Parallel Problem Solving from Nature (pp. 307-313). Berlin: Springer-Verlag.
47.
Todd, P.M., & Wilson, S.W. (1993). Environment structure and adaptive behavior from the ground up. In J.-A. Meyer, H. L. Roitblat, & S. W Wilson (Eds.), From animals to animats 2: Proceedings of the Second International Conference on Simulation of Adaptive Behavior (pp. 11-20). Cambridge, MA: MIT Press/Bradford Books.
48.
Todd, P.M., Wilson, S.W., Somayaji, A.B., & Yanco, H.A. (1994). The blind breeding the blind: Adaptive behavior without looking. In D. Cliff, P Husbands, J.-A. Meyer, & S. W Wilson (Eds.), From animals to animats 3: Proceedings of the Third International Conference on Simulation of Adaptive Behavior (pp. 228-237). Cambridge, MA: MIT Press/Bradford Books.
49.
Toquenaga, Y., Kajitani, I., & Hoshino, T. (1994). Egrets of a feather flock together. In R. A. Brooks & P Maes (Eds.), Artificial Lift, IV: Proceedings of the Fourth International Workshop on the Synthesis and Simulation of Living Systems (pp. 140-151). Cambridge, MA: MIT Press/Bradford Books.
50.
Werner, G.M., & Dyer, M.G. (1992). Evolution of communication in artificial organisms . In C. G. Langton, C. Taylor, J. D. Farmer, & S. Rasmussen (Eds.), Artificial life II (pp. 659-687). Reading, MA: Addison-Wesley
51.
Werner, G.M., & Dyer, M.G. (1993). Evolution of herding behavior in artificial animals . In J.-A. Meyer, H. L. Roitblat, & S. W Wilson (Eds.), From animals to animats 2: Proceedings of the Second International Conference on Simulation of Adaptive Behavior (pp. 393-399). Cambridge, MA: MIT Press/Bradford Books.
52.
Wilson, S.W. (1985). Knowledge growth in an artificial animal. Iri J. J. Grefenstette (Ed.), Proceedings of an International Conference on Genetic Algorithms and Their Applications (pp. 16-23). Pittsburgh, PA: Carnegie-Mellon University.
53.
Wilson, S.W. (1991). The animat path to AI. In J.-A. Meyer & S. W. Wilson (Eds.), From animals to animats: Proceedings of the First International Conference on Simulation of Adaptive Behavior (pp. 15-21). Cambridge, MA : MIT Press/Bradford Books.
Yamauchi, B.M., & Beer, R.D. (1994). Sequential behavior and learning in evolved dynamical neural networks. Adaptive Behavior, 2(3), 219-246.
56.
Yeager, L. (1994). Computational genetics, physiology, metabolism, neural systems, learning, vision, and behavior or Poly World: Life in a new context. In C. G. Langton (Ed.), Artificial life III (pp. 263-298). Reading, MA: Addison-Wesley.
