Much research in artificial intelligence is concerned with the development of autonomous
agents that can interact effectively with other agents. An important aspect of such agents is …

Chapter 1 Page 1 In Leake, D., ed., 1996, Case-Based Reasoning: Experiences, Lessons, and
Future Directions. Menlo Park: AAAI Press/MIT Press, 1996. Chapter 1 CBR in Context: The …

Predicting human behavior from a small amount of training examples is a challenging
machine learning problem. In this thesis, we introduce the principle of maximum causal …

Heuristic search methods promise to find shortest paths for path-planning problems faster
than uninformed search methods. Incremental search methods, on the other hand, promise …

Case-based reasoning (CBR) is an approach to problem solving that emphasizes the role of
prior experience during future problem solving (ie, new problems are solved by reusing and …

The central task for experience management is the retrieval of reusable experience. This
retrieval task requires additional knowledge besides the experience itself. It requires …

Agents operating in the real world often have limited time available for planning their next
actions. Producing optimal plans is infeasible in these scenarios. Instead, agents must be …

In this paper we address the problem of automatically generating job workflows for the Grid.
These workflows describe the execution of a complex application built from individual …

The ultimate objective in planning is to construct plans for execution. However, when a plan
is executed in a real environment it can encounter differences between the expected and …

In this paper we present an agent-based application of a wireless tourist guide that
combines the Beliefs-Desires-Intentions approach with learning capabilities of Case Base …