Weadvance a knowledge-based learning method that augments conventional generalization to permit concept acquisition in failure domains. These are domains in whichlearning must pro...
This paper presents a motivational system for an autonomous robot which is designed to regulate human-robot interaction. The mode of social interaction is that of a caretaker-infa...
We describe a system for specifying the effects of actions. Unlike those commonly used in AI planning, our system uses an action description language that allows one to specify th...
RecentlyFtwo new backtracking algorithmsF dynamic backtracking (DB)and partial order dynamicbacktracking (PDB) have been presented. These algorithms have the property to be additi...
We propose a frameworkfor robot programming which allows the seamless integration of explicit agent programming with decision-theoretic planning. Specifically, the DTGolog model a...
Craig Boutilier, Raymond Reiter, Mikhail Soutchans...
This paper describes STAGE, a learning approach to automatically improving search performance on optimization problems.STAGElearns an evaluation function which predicts the outcom...
Research in belief revision has been dominated by work that lies firmly within the classic AGM paradigm, characterized by a well-known set of postulates governing the behavior of ...
Models for sequential decision making under uncertainty (e.g., Markov decision processes,or MDPs) have beenstudied in operations research for decades. The recent incorporation of ...
Poker is an interesting test-bed for artificial intelligence research. It is a game of imperfect knowledge, where multiple competing agents must deal with risk management, agent m...
Darse Billings, Denis Papp, Jonathan Schaeffer, Du...