One of the most challenging aspects of reasoning, planning, and acting in a multi-agent domain is reasoning about what the agents know about the knowledge of their fellows, and to...
Chitta Baral, Gregory Gelfond, Tran Cao Son, Enric...
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 t...
Both explanation-based and inductive learning techniques have proven successful in a variety of distributed domains. However, learning in multi-agent systems does not necessarily ...
This paper reports on a fertile marriage between madAgents, a Java and Prolog based multi-agent platform, and EVOLP, a logic programming based language to represent and reason abou...
One way to solve the knowledge acquisition bottleneck is to have ways to translate natural language sentences and discourses to a formal knowledge representation language, especia...