MATE: The multi-agent test environment

In this report we present the Multi-Agent Test Environment, MATE. MATE is a collection of experiment management tools for assisting in the design, testing, and evaluation of distributed problem-solvers. It provides the experimenter with an automated tool for executing and monitoring experiments choosing among rule bases, number of agents, communication strategies, and inference engines. Using MATE the experimenter can run a series of distributed problem-solving experiments without human intervention

Effective Approximations for Multi-Robot Coordination in Spatially Distributed Tasks

Although multi-robot systems have received substantial research attention in recent years, multi-robot coordination still remains a difficult task. Especially, when dealing with spatially distributed tasks and many robots, central control quickly becomes infeasible due to the exponential explosion in the number of joint actions and states. We propose a general algorithm that allows for distributed control, that overcomes the exponential growth in the number of joint actions by aggregating the effect of other agents in the system into a probabilistic model, called subjective approximations, and then choosing the best response. We show for a multi-robot grid-world how the algorithm can be implemented in the well studied Multiagent Markov Decision Process framework, as a sub-class called spatial task allocation problems (SPATAPs). In this framework, we show how to tackle SPATAPs using online, distributed planning by combining subjective agent approximations with restriction of attention to current tasks in the world. An empirical evaluation shows that the combination of both strategies allows to scale to very large problems, while providing near-optimal solutions

Effective Approximations for Spatial Task Allocation Problems

Although multi-robot systems have received substantial research attention in recent years, multi-robot coordination still remains a difficult task. Especially, when dealing with spatially distributed tasks and many robots, central control quickly becomes infeasible due to the exponential explosion in the number of joint actions and states. We propose a general algorithm that allows for distributed control, that overcomes the exponential growth in the number of joint actions by aggregating the effect of other agents in the system into a probabilistic model, called subjective approximations, and then choosing the best response. We show for a multi-robot grid-world how the algorithm can be implemented in the well studied Multiagent Markov Decision Process framework, as a sub-class called spatial task allocation problems (SPATAPs). In this framework, we show how to tackle SPATAPs using online, distributed planning by combining subjective agent approximations with restriction of attention to current tasks in the world. An empirical evaluation shows that the combination of both strategies allows to scale to very large problems, while providing near-optimal solutions

Efficient Credit Assignment through Evaluation Function Decomposition

Evolutionary methods are powerful tools in discovering solutions for difficult continuous tasks. When such a solution is encoded over multiple genes, a genetic algorithm faces the difficult credit assignment problem of evaluating how a single gene in a chromosome contributes to the full solution. Typically a single evaluation function is used for the entire chromosome, implicitly giving each gene in the chromosome the same evaluation. This method is inefficient because a gene will get credit for the contribution of all the other genes as well. Accurately measuring the fitness of individual genes in such a large search space requires many trials. This paper instead proposes turning this single complex search problem into a multi-agent search problem, where each agent has the simpler task of discovering a suitable gene. Gene-specific evaluation functions can then be created that have better theoretical properties than a single evaluation function over all genes. This method is tested in the difficult double-pole balancing problem, showing that agents using gene-specific evaluation functions can create a successful control policy in 20 percent fewer trials than the best existing genetic algorithms. The method is extended to more distributed problems, achieving 95 percent performance gains over tradition methods in the multi-rover domain

Multi-Hyb: a hybrid algorithm for solving DisCSPs with complex local problems.

A coarse-grained Distributed Constraint Satisfaction Problem (DisCSP) is a constraint problem where several agents, each responsible for solving one part (a complex local problem), cooperate to determine an overall solution. Thus, agents solve the overall problem by finding a solution to their complex local problem which is compatible with the solutions proposed by other agents for their own local problems. Several approaches to solving DisCSPs have been devised and can be classified as systematic search and local search techniques. We present Multi-Hyb, a two-phase hybrid algorithm for solving coarse-grained DisCSPs which uses both systematic and local search during problem solving. Phase 1 generates key partial solutions to the global problem using systematic search. Concurrently, a penalty-based local search algorithm attempts to find a global solution to the problem using these partial solutions. If a global solution is not found in phase 1, the information learnt from phase 1 is used to inform the search carried out during the next phase. Phase two runs a systematic search algorithm on complex variables guided by the following knowledge obtained in phase 1: (i) partial solutions and; (ii) complex local problems which appear more difficult to satisfy. Experimental evaluation demonstrates that Multi-Hyb is competitive in several problem classes in terms of: (i) the communication cost and (ii) the computational effort needed

FMAP: Distributed Cooperative Multi-Agent Planning

This paper proposes FMAP (Forward Multi-Agent Planning), a fully-distributed multi-agent planning method that integrates planning and coordination. Although FMAP is specifically aimed at solving problems that require cooperation among agents, the flexibility of the domain-independent planning model allows FMAP to tackle multi-agent planning tasks of any type. In FMAP, agents jointly explore the plan space by building up refinement plans through a complete and flexible forward-chaining partial-order planner. The search is guided by h D T G , a novel heuristic function that is based on the concepts of Domain Transition Graph and frontier state and is optimized to evaluate plans in distributed environments. Agents in FMAP apply an advanced privacy model that allows them to adequately keep private information while communicating only the data of the refinement plans that is relevant to each of the participating agents. 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