Integrating CBR components within a Case-Based Planner David B. Leake and Andrew Kinley

AAAI Conferences

Case-based reasoning provides opportunities for integrations both with other reasoning processes and within the CBR process itself. Case-based reasoning intelligent components (Riesbeck 1996), integrated with other reasoning systems, can augment those systems by monitoring their processing and learning from their successes and failures to increase the speed or quality of reasoning. Conversely, other reasoning methods integrated into case-based reasoning systems can help to support the fundamental subprocesses of CBR, such as case adaptation and similarity assessment. Integrated reasoning systems can be characterized in multiple ways. One characterization, as proposed in the Call for Papers for the AAAI-98 Workshop Case-Based Reasoning Integrations, describes the control relationships of the integrated components: master-slave, slave-master, or collaborative.


Combining Reasoning Modes, Levels, and Styles through Internal CBR* David B. Leake and Andrew Kinley

AAAI Conferences

The reasoning processes of artificial intelligence systems can be described along multiple dimensions, such as the reasoning mode or paradigm the system uses (e.g., rule-based reasoning or case-based reasoning), the style of reasoning within that paradigm (e.g., transformational or derivational approaches to case-based reasoning), and the level at which that reasoning is applied (e.g., domain-level reasoning or metareasoning). Their combination provides interesting opportunities.



Applying Machine Learning to Discourse Processing

AI Magazine

The topics of the eight symposia were (1) Applying Machine Learning to Discourse Processing, (2) Integrating Robotic Research: Taking the Next Leap, (3) Intelligent Environments, (4) Intelligent Text Summarization, (5) Interactive and Mixed-Initiative Decision-Theoretic Systems, (6) Multimodal Reasoning, (7) Prospects for a Common-Sense Theory of Causation, and (8) Satisficing Models. In addition, two tutorials provided an overview of various machine-learning techniques and how some have been applied to other areas of NLP. During the discussion and panel sessions, a number of open problems were raised. There was much discussion on the availability of annotated corpora in the public domain to facilitate the application of supervised machine-learning techniques and allow the comparison of results obtained using different learning approaches. The American Association for Artificial Intelligence, in cooperation with Stanford University's Department of Computer Science, held the 1998 Spring Symposium Series on 23 to 25 March at Stanford University.


The AAAI Spring Symposia

AI Magazine

The Association for the Advancement of Artificial Intelligence, in cooperation with Stanford University's Department of Computer Science, held the 1998 Spring Symposium Series on 23 to 25 March at Stanford University. The topics of the eight symposia were (1) Applying Machine Learning to Discourse Processing, (2) Integrating Robotic Research: Taking the Next Leap, (3) Intelligent Environments, (4) Intelligent Text Summarization, (5) Interactive and Mixed-Initiative Decision-Theoretic Systems, (6) Multimodal Reasoning, (7) Prospects for a Common-Sense Theory of Causation, and (8) Satisficing Models.