Supporting Conversational Case-Based Reasoning in an Integrated Reasoning Framework

摘要

Conversational case-based reasoning (CCBR) has been successfully used to assist in case retrieval tasks. However, behavioral limitations of CCBR motivate the search for integrations with other reasoning approaches. This paper briefly describes our group's ongoing efforts towards enhancing the inferencing behaviors of a conversational case-based reasoning development tool named NACODAE. In particular, we focus on integrating NACODAE with machine learning, model-based reasoning, and generative planning modules. This paper defines CCBR, briefly summarizes the integrations, and explains how they enhance the overall system.