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6 papers

1 author row

AAAI Conference 2007 Conference Paper

Temporal Difference and Policy Search Methods for Reinforcement Learning: An Empirical Comparison

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Reinforcement learning (RL) methods have become popular in recent years because of their ability to solve complex tasks with minimal feedback. Both genetic algorithms (GAs) and temporal difference (TD) methods have proven effective at solving difﬁcult RL problems, but few rigorous comparisons have been conducted. Thus, no general guidelines describing the methods’ relative strengths and weaknesses are available. This paper summarizes a detailed empirical comparison between a GA and a TD method in Keepaway, a standard RL benchmark domain based on robot soccer. The results from this study help isolate the factors critical to the performance of each learning method and yield insights into their general strengths and weaknesses.

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AAAI Conference 2007 Conference Paper

Uncertainty in Preference Elicitation and Aggregation

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Uncertainty arises in preference aggregation in several ways. There may, for example, be uncertainty in the votes or the voting rule. Such uncertainty can introduce computational complexity in determining which candidate or candidates can or must win the election. In this paper, we survey recent work in this area and give some new results. We argue, for example, that the set of possible winners can be computationally harder to compute than the necessary winner. As a second example, we show that, even if the unknown votes are assumed to be single-peaked, it remains computationally hard to compute the possible and necessary winners, or to manipulate the election.

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AAAI Conference 2000 Conference Paper

Inter-Layer Learning Towards Emergent Cooperative Behavior

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As applications for artificially intelligent agents increase in complexity we can no longer rely on clever heuristics and hand-tuned behaviors to develop their programming. Even the interaction between various components cannot be reduced to simple rules, as the complexities of realistic dynamic environments become unwieldy to characterize manually. To cope with these challenges, we propose an architecture for inter-layer learning where each layer is constructed with a higher level of complexity and control. Using RoboCup soccer as a testbed, we demonstrate the potential of this architecture for the development of effective, cooperative, multi-agent systems. At the lowest layer, individual basic skills are developed and refined in isolation through supervised and reinforcement learning techniques. The next layer uses machine learning to decide, at any point in time, which among a subset of the first layer tasks should be executed. This process is repeated for successive layers, thus providing higher levels of abstraction as new layers are added. The inter-layer learning architecture provides an explicit learning model for deciding individual and cooperative tactics in a dynamic environment and appears to be promising in real-time competition.

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AAAI Conference 1994 Conference Paper