Game-Theoretic Agent Programming

The main goal of the project is to develop high-level formalisms and techniques for programming systems of multiple agents in uncertain and partially observable environments. We want to develop such agent programming languages by combining formalisms from reasoning about actions (which have nice software engineering features), planning under uncertainty (for modeling uncertainty and partial observability), and game theory (for modeling cooperation and competition in multi-agent systems). More precisely, we aim at an approach where the logic- oriented languages for reasoning about actions around Golog and around the action language C+ are combined with multi-agent variants of POMDPs and with partially observable Markov games, for cooperation and competition, respectively (in which planning under uncertainty and partial observability is combined with game theory). We plan to design algorithms that are based on reductions to answer set programming, and to develop implementations on top of existing answer set programming technology, such as DLV and SMODELS.

The main goal of the project is to develop high-level formalisms and techniques for programming systems of multiple agents in uncertain and partially observable environments. We want to develop such agent programming languages by combining formalisms from reasoning about actions (which have nice software engineering features), planning under uncertainty (for modeling uncertainty and partial observability), and game theory (for modeling cooperation and competition in multi-agent systems). More precisely, we aim at an approach where the logic- oriented languages for reasoning about actions around Golog and around the action language C+ are combined with multi-agent variants of POMDPs and with partially observable Markov games, for cooperation and competition, respectively (in which planning under uncertainty and partial observability is combined with game theory). We plan to design algorithms that are based on reductions to answer set programming, and to develop implementations on top of existing answer set programming technology, such as DLV and SMODELS.