Modular Utility Representation for Decision-Theoretic Planning

Specification of objectives constitutes a central issue in knowledge representation for planning. Decision-theoretic approaches require that representations of objectives possess a firm semantics in terms of utility functions, yet provide the flexible compositionality needed for practical preference modeling for planning systems. Modularity, or separability in specification, is the key representational feature enabling this flexibility. In the context of utility specification, modularity corresponds exactly to well-known independence concepts from multiattribute utility theory, and leads directly to approaches for composing separate preference specifications. Ultimately, we seek to use this utility-theoretic account to justify and improve existing mechanisms for specification of preference information, and to develop new representations exhibiting tractable specification and flexible composition of preference criteria.

From knowledge bases to decision models

In recent years there has been a growing interest among AI researchers in probabilistic and decision modeling, spurred by significant advances in representation and computation with network modeling formalisms. In applying these techniques to decision-support tasks, fixed network models have proven inadequately expressive to handle a broad range of situations. Therefore, many researchers have sought to combine the flexibility of general-purpose knowledge representation languages with the normative status and well-understood computational properties of decision-modeling formalisms and algorithms. One approach is to encode general knowledge in an expressive language, then dynamically construct a decision model for each particular situation or problem instance. We have developed several systems adopting this approach, which illustrate a variety of interesting techniques and design issues.