%Date: Fri, 16 Feb 1996 09:20:41 CST %From: Thomas Haynes %Subject: Re: Ecology simulations % %Tom Haynes %haynes@euler.mcs.utulsa.edu %http://euler.mcs.utulsa.edu/~haynes/haynes.html @TechReport{miller:1994:CPE, author = "Geoffrey F. Miller and Dave Cliff", title = "Co-Evolution of Pursuit and Evasion I: Biological and game-Theoretic Foundations", institute = "School of Cognitive and Computing Sciences, University of Sussex", number = "CSRP311", month = "August", year = "1994", notes = "Submitted to Adaptive Behavior. A copy of the paper is ftp'able", } @InProceedings{hahn:1994:p-p, author = "Mark S. Hanh", title = "Simulating Evolution In a Kolmogorov Predator-Prey Model With Genetic Extensions", booktitle = "Artificial Life at Stanford 1994", year = "1994", editor = "John R. Koza", pages = "44--53", address = "Stanford, California, 94305-3079 USA, Phone 415-329-1217 or 800-533-2670", month = jun, organisation = "Stanford University", publisher = "Stanford Bookstore", keywords = "genetic algorithms", ISBN = "0-18-182105-2", notes = "This volume contains 22 papers written and submitted by students describing their term projects for the course in artificial life (Computer Science 425) at Stanford University offered during the spring quarter quarter 1994 http://www-cs-faculty.stanford.edu/~koza/cs425.html", } @TechReport{smith:1991:UMP, author = "Mark Smith", title = "Using Massifvely-Parallel Supercomputers to Model Stochastic Spatial Predator-Prey Systems", institute = "University of Edinburgh", number = "EPCC-TR91-06", year = "17th April 1991", notes = "Says to be published in International Journal of Ecological Modelling. A copy of the paper is ftp'able", } @InProceedings{Iba:1993:elpbsc, author = "H. Iba and H. de Garis and T. Higuchi", title = "Evolutionary learning of predatory behaviors based on structured classifiers", booktitle = "From Animals to Animats 2: Proceedings of the Second International Conference on Simulation of Adaptive Behavior", year = "1993", editor = "J. A. Meyer and H. L. Roitblat and S. W. Wilson", volume = "1", publisher = "The MIT Press", keywords = "genetic algorithms, genetic programming", } @InProceedings{Haynes:1995:EBS, author = "Thomas Haynes and Sandip Sen", title = "Evolving behavioral strategies in Predators and Prey", booktitle = "IJCAI-95 Workshop on Adaptation and Learning in Multiagent Systems", year = "1995", pages = "32--37", editor = "Sandip Sen", abstract = "The predator/prey domain is utilized to conduct research in Distributed Artificial Intelligence. Genetic Programing is used to evolve behavioral strategies for the predator agents. To further the utility of the predator strategies, the prey population is allowed to evolve at the same time. The expected competitive learning cycle did not surface. This failing is investigated, and a simple prey algorithm surfaces, which is consistently able to evade capture from the predator algorithms.", } @InProceedings{Haynes:1995:ECS, title = "Evolving Cooperation Strategies", author = "Thomas Haynes and Roger Wainwright and Sandip Sen", pages = "450", note = "(poster)", booktitle = "Proceedings of the First International Conference on Multi--Agent Systems", year = "1995", publisher = "MIT Press", address = "San Francisco, CA", editor = "Victor Lesser", abstract = "The identification, design, and implementation of strategies for cooperation is a central research issue in the field of Distributed Artificial Intelligence (DAI). We propose a novel approach to the construction of cooperation strategies for a group of problem solvers based on the Genetic Programming (GP) paradigm. GP's are a class of adaptive algorithms used to evolve solution structures that optimize a given evaluation criterion. Our approach is based on designing a representation for cooperation strategies that can be manipulated by GPs. We present results from experiments in the predator-prey domain, which has been extensively studied as an easy-to-describe but difficult-to-solve cooperation problem domain. They key aspect of our approach is the minimal reliance on domain knowledge and human intervention in the construction of good cooperation strategies. Promising comparison results with prior systems lend credence to the viability of this approach.", keywords = "evolutionary computation, cooperation strategies", } @Article{Haynes:1995:EMC, author = "Thomas Haynes and Sandip Sen and Dale Schoenefeld and Roger Wainwright", title = "Evolving Multiagent Coordination Strategies with Genetic Programming", journal = "Artificial Intelligence", year = "1995", note = "(submitted for review)", } @InProceedings{Haynes:1995:ET, author = "Thomas Haynes and Sandip Sen and Dale Schoenefeld and Roger Wainwright", title = "Evolving a Team", booktitle = "Working Notes for the AAAI Symposium on Genetic Programming", year = "1995", editor = "E. V. Siegel and J. R. Koza", publisher = "AAAI", address = "Cambridge, MA", month = Nov, } @InProceedings{Haynes:1995:STG, author = "Thomas Haynes and Roger Wainwright and Sandip Sen and Dale Schoenefeld", title = "Strongly typed genetic programming in evolving cooperation strategies", booktitle = "Proceedings of the Sixth International Conference on Genetic Algorithms", editor = "Larry Eshelman", year = "1995", address = "San Francisco, CA", publisher = "Morgan Kaufmann Publishers, Inc.", pages = "271--278", } @InCollection{Haynes:1996:EBS, author = "Thomas Haynes and Sandip Sen", title = "Evolving Behavioral Strategies in Predators and Prey", editor = "Gerhard Wei{\ss} and Sandip Sen", booktitle = "Adaptation and Learning in Multiagent Systems", year = "1996", month = "Spring", publisher = "Springer Verlag", series = "Lecture Notes in Artificial Intelligence", address = "Berlin", } @InProceedings{Haynes:1996:LCC, author = "Thomas Haynes and Kit Lau and Sandip Sen", title = "Learning Cases to Compliment Rules for Conflict Resolution in Multiagent Systems", pages = "", booktitle = "Working Notes for the AAAI Symposium on Adaptation, Co-evolution and Learning in Multiagent Systems", year = "1996", month = Mar, address = "Stanford University, CA", editor = "Sandip Sen", } @InProceedings{Manela:1993:DGP, author = "Mauro Manela and J. A. Campbell", title = "Designing Good Pursuit Problems as Testbeds for {D}istributed {AI}: a Novel Application of {G}enetic {A}lgorithms", booktitle = "Fifth European Workshop on Modelling Autonomous Agents in a Multi-Agent World", year = "1993", month = aug # " 24-27", address = "Neuch\^{a}tel, Switzerland", } @InProceedings{Korf:1992:SSP, author = "Richard E. Korf", title = "A Simple Solution to Pursuit Games", booktitle = "Working Papers of the 11th International Workshop on Distributed Artificial Intelligence", pages = "183--194", month = feb, year = "1992", } @InProceedings{Levy:1992:GTA, author = "Ran Levy and Jeffrey S. Rosenschein", title = "A Game Theoretic Approach to the Pursuit Problem", booktitle = "Working Papers of the 11th International Workshop on Distributed Artificial Intelligence", pages = "195--213", month = feb, year = "1992", } @InProceedings{Maio:1995:UMA, title = "Unsupervised Multi-Agent Exploration Of Structured Environments", author = "Dario Maio and Stefano Rizzi", pages = "269--275", booktitle = "Proceedings of the First International Conference on Multi--Agent Systems", year = "1995", publisher = "MIT Press", address = "San Francisco, CA", editor = "Victor Lesser", abstract = "Exploration is a central issue for autonomous agents which must carry out navigation tasks in environments whose description is not known a priori. In our approach the environment is described, from a symbolic point of view, by means of a graph; clustering techniques allow for further levels of abstraction to be defined, leading to a multi-layered representation. In this work we propose an unsupervised exploration algorithm in which several agents cooperate to acquire knowledge of the environment at the different abstraction levels; a broadcast model is adopted for inter-agent communication. All agents are structurally equal and pursue the same local exploration strategy; nevertheless, the existence of multiple levels of abstraction in the environment representation allows for the agents' behaviours to differentiate. Agents carry out exploration at different abstraction levels, aimed at reproducing an ideal exploration profile; each agent selects dynamically its exploration level, based on the current demand.", } @InProceedings{Singh:1990:EAC, author = "Munindar P. Singh", title = "The effect of agent control strategy on the performance of a {DAI} pursuit problem", booktitle = "Working Papers of the 10th International Workshop on Distributed Artificial Intelligence", month = oct, year = "1990", } @InProceedings{Stephens:1990:EAC, author = "Larry M. Stephens and Matthias B. Merx", title = "The Effect of Agent Control Strategy on the Performance of a {DAI} Pursuit Problem", year = "1990", booktitle = "Proceedings of the 1990 Distributed AI Workshop", month = Oct, } @InProceedings{Vidal:1995:RAM, title = "Recursive Agent Modeling using Limited Rationality", author = "Jos\'{e} M. Vidal and Edmund H. Durfee", pages = "376--383", booktitle = "Proceedings of the First International Conference on Multi--Agent Systems", year = "1995", publisher = "MIT Press", address = "San Francisco, CA", editor = "Victor Lesser", abstract = "We present an algorithm that an agent can use for determining which of its nested, recursive models of other agents are important to consider when choosing an action. Pruning away less important models allows an agent to take its {"}best{"} action in a timely manner, given its knowledge, computational abilities, and time constraints. We describe a theoretical framewwork, based on situations, for talking about recursive agent models and the strategies and expected strategies associated with them. This framework allows us to rigorously define the gain of continuing deliberation versus taking action. The expected gain of computational actionss is used to guide the pruning of the nested model structure. We have implemented our approach on a canonical multi-agent problem, the pursuit task, to illustrate how real-time, mulit-agent decision-making can be based on a principled, combinatorial model. Test results show a marked decrease in deliberation time while maintaining a good performance level.", keywords = "Algorithms for multi-agent interaction in time-constrained systems, Conceptual and theoretical foundations of multi-agent systems.", } --- Tom Haynes haynes@euler.mcs.utulsa.edu http://euler.mcs.utulsa.edu/~haynes/haynes.html