Many learning systems suffer from the utility problem; that is, that time after learning is greater than time before learning. Discovering how to assure that learned knowledge wil...
We formalize a model for supervised learning of action strategies in dynamic stochastic domains and show that PAC-learning results on Occam algorithms hold in this model as well. W...
We propose an agent-based framework for assisting and simplifying person-to-person communication for information gathering tasks. As an example, we focus on locating experts for a...
GSAT is a randomized greedy local repair procedure that was introduced for solving propositional satis ability and constraint satisfaction problems. We present an improvement to G...
We investigate the range of applicability of Baker's approach to the frame problem using an action language. We show that for temporal projection and deterministic domains, B...
The ideas of dependency directed backtracking (DDB) and explanation based learning (EBL) have developed independently in constraint satisfaction, planning and problem solving comm...
Partial-Order Causal Link planners typically take a "least-commitment" approach to some decisions (notably, step ordering), postponing those decisions until constraints ...
Previous work suggests that reminding a conversational partner of mutually known information depends on the conversants' attentional state, their resource limits and the reso...