Intentions have been widely studied in AI, both in the context of decision-making within individual agents and in multiagent systems. Work on intentions in multi-agent systems has...
The behavior of a complex system often depends on parameters whose values are unknown in advance. To operate effectively, an autonomous agent must actively gather information on t...
Li Ling Ko, David Hsu, Wee Sun Lee, Sylvie C. W. O...
Computer agents are increasingly deployed in settings in which they make decisions with people, such as electronic commerce, collaborative interfaces, and cognitive assistants. Ho...
Raz Lin, Sarit Kraus, Yinon Oshrat, Ya'akov (Kobi)...
Recent advancements in model-based reinforcement learning have shown that the dynamics of many structured domains (e.g. DBNs) can be learned with tractable sample complexity, desp...
Thomas J. Walsh, Sergiu Goschin, Michael L. Littma...
Modern complex games and simulations pose many challenges for an intelligent agent, including partial observability, continuous time and effects, hostile opponents, and exogenous ...
Research on information extraction (IE) seeks to distill relational tuples from natural language text, such as the contents of the WWW. Most IE work has focussed on identifying st...
Multi-Agent Plan Recognition (MAPR) seeks to identify the dynamic team structures and team behaviors from the observations of the activity-sequences of a set of intelligent agents...
Mini-Bucket Elimination (MBE) is a well-known approximation algorithm deriving lower and upper bounds on quantities of interest over graphical models. It relies on a procedure tha...
Ontology matching is the problem of determining correspondences between concepts, properties, and individuals of different heterogeneous ontologies. With this paper we present a n...
Mathias Niepert, Christian Meilicke, Heiner Stucke...