The Third International Conference on Case-Based Reasoning (ICCBR 1999)

AI Magazine

The Third International Conference on Case-Based Reasoning was held at the Seeon Monastery, Bavaria, 27 to 30 July 1999. About 120 researchers from 21 countries attended. The conference included 4 workshops; 3 invit-ed talks; 24 technical presentations; a poster session; and an Industry Day, where the focus was on mature technologies and applications in industry.


Case Base Engineering for Large Scale Industrial Applications

AAAI Conferences

In recent years, case-based reasoning (CBR) has emerged as a promising technology for decision support (Allen 1994). CBR systems aid a decision maker (DlVl) retrieving past experiences and using their outcome to solve new decision problems (Kolodner 1993). Each nugget of experience can be encoded as a problem description and solution pair, that is, a case. CBR technology is attractive to the industry because it is easily understood, there is apparent availability of problem-solving cases throughout the organization, and its capability to reason with partial input and grow by simply adding cases. Consequently, a number of firms have adopted the technology and successfully developed diagnostic and planning applications (Allen, 1994).


Two 1 Paradigms of Complementary CBR Integrations

AAAI Conferences

Introduction A wide variety of multiple-paradigm reasoning systems have been developed in recent years. Case-based reasoning (CBR) is very frequently one of the reasoning paradigms in such integrations. Three justifications for the integration of multiple reasoning paradigms can be distinguished. First, the domain may be characterized by multiple knowledge representations. Reasons for multiple knowledge representations include the following: Institutional.


AI (hierarchical

AI Magazine

This research was motivated by the widely held belief that constructing an automatic program synthesis system that can accept a high-level description of a problem for an arbitrary domain and generate code for the problem completely automatically is pragmatically impossible. However, by focusing on a well-defined domain, it is possible to incorporate sufficient knowledge within a system so that it can communicate with an end user at the level of his(her) application and automatically generate a program from a problem specification. Such knowledge-based systems often employ a catalog of transformational rules that progressively refine an abstract specification into a concrete implementation. A major research issue in such systems is how to increase the efficiency of the systems by controlling the application of rules and avoiding repetitive traversal of the search space. In my Ph.D. dissertation (Bhansali 1991), I develop an integrated knowledge-based framework for efficiently synthesizing programs by bringing together ideas from the fields of software engineering (software reuse, domain modeling) and The knowledge base consists of three subcomponents: a concept dictionary, a library of reusable components, and a layered rule base.


Domain-Based Program Synthesis Using Planning and Derivational Analogy

AI Magazine

In my Ph.D. dissertation (Bhansali 1991), I develop an integrated knowledge-based framework for efficiently synthesizing programs by bringing together ideas from the fields of software engineering (software reuse, domain modeling) and AI (hierarchical planning, analogical reasoning). Based on this framework, I constructed a prototype system, APU, that can synthesize UNIX shell scripts from a high-level specification of problems typically encountered by novice shell programmers. An empirical evaluation of the system's performance points to certain criteria that determine the feasibility of the derivational analogy approach in the automatic programming domain when the cost of detecting analogies and recovering from wrong analogs is considered.