Most Artificial Intelligence programs lack generality because they reason with a single domain theory that is tailored for a specific task and embodies a host of implicit assumpti...
We present tractable, exact algorithms for learning actions' effects and preconditions in partially observable domains. Our algorithms maintain a propositional logical repres...
Constraint programming provides a declarative approach to solving combinatorial (optimization) problems. The user just states the problem as a constraint satisfaction problem (CSP)...
We propose a framework for policy generation in continuoustime stochastic domains with concurrent actions and events of uncertain duration. We make no assumptions regarding the co...
We present a general theory of Gifford-style type and effect annotations, where effect annotations are sets of effects. Generality is achieved by recourse to the theory of algebra...