Distributed Artificial Intelligence (DAI) has existed as a subfield of AI for less than two decades. DAI is

The predator/prey domain is utilized to conduct research in Distributed Artificial Intelligence. Genetic Programming 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.

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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 capabilities, and time constraints. We describe a theoretical framework, 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 actions 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, multi-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.

Groups of agents following fixed behavioral rules can be limited in performance and efficiency. Adaptability and flexibility are key components of intelligent behavior which allow agent groups to improve performance in a given domain using prior problem solving experience. We motivate the usefulness of individual learning by group members in the context of overall group behavior. In particular, we propose a framework in which individual group members learn cases from problem-solving experiences to improve their model of other group members. We use a testbed problem from the distributed AI literature to show that simultaneous learning by group members can lead to significant improvement in group performance and efficiency over agent groups following static behavioral rules.

We address the issue of rational communicative behavior among autonomous intelligent agents that have to make decisions as to what, to whom, and how to communicate. We treat communicative actions as aimed at increasing the efficiency of interaction among agents. We postulate that a rational speaker design a speech act so as to maximally increase the benefit obtained as the result of the interaction. We quantify the gain in the quality of interaction as the expected utility, and we present a framework that allows an agent to compute the expected utility of various communicative actions. Our framework uses the Recursive Modeling Method as the representation of the agent's state of knowledge, including the agent's preferences, abilities and beliefs about the world, as well as the beliefs the agent has about the other agents, the beliefs it has about the other agents ' beliefs, and so on. A decision-theoretic pragmatics of a communicative act can be then defined as the transformation it induces on the agent's state of knowledge about its decision-making situation. This transformation leads to a change in the quality of the interaction, expressed in terms of the benefit to the agent. We analyze decision-theoretic pragmatics of a number of important communicative acts, and investigate their expected utility using examples.

We introduce a cooperative co--evolutionary system to facilitate the development of teams of heterogeneous agents. We believe that k different behavioral strategies for controlling the actions of a group of k agents can combine to form a cooperation strategy which efficiently achieves global goals. We both examine the on-line adaption of behavioral strategies utilizing genetic programming and demonstrate the successful co-evolution of cooperative individuals. We present a new crossover mechanism for genetic programming systems in order to facilitate the evolution of more than one member in the team during each crossover operation. Our goal is to reduce the time needed to evolve an effective team.

The design and development ofbehavioral strategies to coordinate the actions of multiple agents is a central issue in multiagent systems research. We propose a novel approach ofevolving, rather than handcrafting, behavioral strategies. The evolution scheme usedisavariant ofthe Genetic Programming (GP) paradigm. As a proof of principle, we evolve behavioral strategies in the predator{prey domain that has been studied widely in the DistributedArti cial Intelligence community. Weusethe GPto evolve behavioral strategies for individual agents, as prior literature claims that communication between predators is not necessary for successfully capturing the prey. Theevolved strategy, when used by each predator, performs better than all but oneofthe handcrafted strategies mentioned in literature. We analyze the shortcomings of each ofthese strategies. The next set of experiments involve co{evolving predators and prey. Toour surprise, a simple prey strategy evolves that consistently evades all of the predator strategies. We analyze the implications of the relative successes of evolution in the two sets of experiments and comment onthe nature of domains for which GPbasedevolutionisaviable mechanism for generating coordination strategies. We conclude withourdesign for concurrent evolution of multiple agent strategies in domains where agents need to communicate with eachother to successfully solve a common problem.

We adopt the decision-theoretic principle of expected utility maximization as a paradigm for designing autonomous rational agents, and present a framework that uses this paradigm to determine the choice of coordinated action. We endow an agent with a specialized representation that captures the agent's knowledge about the environment and about the other agents, including its knowledge about their states of knowledge, which can include what they know about the other agents, and so on. This reciprocity leads to a recursive nesting of models. Our framework puts forth a representation for the recursive models and, under the assumption that the nesting of models is finite, uses dynamic programming to solve this representation for the agent's rational choice of action. Using a decision-theoretic approach, our work addresses concerns of agent decision-making about coordinated action in unpredictable situations, without imposing upon agents pre-designed prescriptions, or protocols, ...

The identi cation, design, and implementation of strategies for cooperation is a central research issue in the eld of Distributed Arti cial Intelligence (DAI). We propose a novel approach tothe construction of cooperation strategies for a group of problem solvers based on the Genetic Programming (GP) paradigm. GPs 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, whichhas been extensively studied as a easy-to-describe but di cult-to-solve cooperation problem domain. The key aspect of our approach isthe minimalreliance on domain knowledge and human intervention in the construction of good cooperation strategies. Promising comparison results withprior systems lend credence to the viabilityofthis approach. Topic areas: Evolutionary computation, cooperation strategies 1