Developing applications that make effective use of machine-readable knowledge sources as promised by the Semantic Web vision is attracting much of current research interest; this v...
Planning algorithms have traditionally been geared toward achievement goals in single-agent environments. Such algorithms essentially produce plans to reach one of a specified se...
In this paper we report on using a relational state space in multi-agent reinforcement learning. There is growing evidence in the Reinforcement Learning research community that a r...
Tom Croonenborghs, Karl Tuyls, Jan Ramon, Maurice ...
AI planning requires the definition of action models using a formal action and plan description language, such as the standard Planning Domain Definition Language (PDDL), as inp...
We report on the performance of an enhanced version of the “Davis-Putnam” (DP) proof procedure for propositional satisfiability (SAT) on large instances derived from realworld...