Smadar Kedar

A unified framework for explanation-based generalization of partially ordered and partially instantiated plans

Abstract Most previous work in explanation-based generalization (EBG) of plans dealt with totally ordered plans. These methods cannot be directly applied to generalizing partially ordered partially instantiated plans, a class of plans that have received significant attention in planning. In this paper we present a natural way of extending the explanation-based generalization methods to partially ordered partially instantiated (POPI) plans. Our development is based on modal truth criteria for POPI plans [3]. We develop explanation structures from these truth criteria, and use them as a common basis to derive a variety of generalization algorithms. Specifically we present algorithms for precondition generalization, order generalization, and possible correctness generalization of POPI plans. The systematic derivation of the generalization algorithms from the modal truth criterion obviates the need for carrying out a separate formal proof of correctness of the EBG algorithms. Our development also systematically explicates the tradeoffs among the spectrum of possible generalizations for POPI plans, and provides an empirical demonstration of the relative utility of EBG in partial ordering, as opposed to total ordering, planning frameworks.

No multimedia without representation

Without represen(a[icsn multimedia will not happen. Computers tire basically deaf, blind, and ignorant, and will remain so for quite some time. Only by creating representations for media con[ent will we be able to construct large, robust multimedia sys[ems that support content-based manipulation of video, audio, and images. Yet the research community that deals with representation (A1 researchers) and the community that deals with media manipulation (multimedia researchers) have, for the most part. not come inro contact. The purpose of this panel is to incite debate about the role of representation of multimedia contenl by bringing these two communities together. Our intention IS to confront rese~rchers at ACM Multimedia’94 with a variety of positions al-rout the need for content representation in largescalc multimedia systems We argue that work in knowledge reprcscn[ation IS essential to the construction of robust multimedia systcrns t{>manage media content.

Tradeoffs in the utility of learned knowledge

Planning systems which make use of domain theories can produce more accurate plans and achieve more goals as the quality of their domain knowledge improves. MTR, a multi-strategy learning system, was designed to learn from system failures and improve domain knowledge used in planning. However, augmented domain knowledge can decrease planning efficiency. We describe how improved knowledge that becomes expensive to use can be approximated to yield calculated tradeoffs in accuracy and efficiency.

Ask how it works: an intelligent interactive manual for devices

We describe Ask How It Works, a prototype interactive intelligent manual for devices, based on novel intelligent training systems called ASK Systems.