Posts Tagged ‘multistrategy learning’

Introspective Reasoning using Meta-Explanations for Multistrategy Learning

In order to learn effectively, a reasoner must not only possess knowledge about the world and be able to improve that knowledge, but it also must introspectively reason about how it performs a given task and what particular pieces of knowledge it needs to improve its performance at the current task. Introspection requires declarative representations of meta-knowledge of the reasoning performed by the system during the performance task, of the system’s knowledge, and of the organization of this knowledge.

This paper presents a taxonomy of possible reasoning failures that can occur during a performance task, declarative representations of these failures, and associations between failures and particular learning strategies. The theory is based on Meta-XPs, which are explanation structures that help the system identify failure types, formulate learning goals, and choose appropriate learning strategies in order to avoid similar mistakes in the future. The theory is implemented in a computer model of an introspective reasoner that performs multistrategy learning during a story understanding task.

Read the paper:

Introspective Reasoning using Meta-Explanations for Multistrategy Learning

by Ashwin Ram, Mike Cox

In Machine Learning: A Multistrategy Approach, Vol. IV, R.S. Michalski and G. Tecuci (eds.), 349-377, Morgan Kaufmann, 1994
www.cc.gatech.edu/faculty/ashwin/papers/git-cc-92-19.pdf

Indexing, Elaboration and Refinement: Incremental Learning of Explanatory Cases

This article describes how a reasoner can improve its understanding of an incompletely understood domain through the application of what it already knows to novel problems in that domain. Case-based reasoning is the process of using past experiences stored in the reasoner’s memory to understand novel situations or solve novel problems. However, this process assumes that past experiences are well understood and provide good “lessons” to be used for future situations. This assumption is usually false when one is learning about a novel domain, since situations encountered previously in this domain might not have been understood completely. Furthermore, the reasoner may not even have a case that adequately deals with the new situation, or may not be able to access the case using existing indices.

We present a theory of incremental learning based on the revision of previously existing case knowledge in response to experiences in such situations. The theory has been implemented in a case-based story understanding program that can (a) learn a new case in situations where no case already exists, (b) learn how to index the case in memory, and (c) incrementally refine its understanding of the case by using it to reason about new situations, thus evolving a better understanding of its domain through experience. This research complements work in case-based reasoning by providing mechanisms by which a case library can be automatically built for use by a case-based reasoning program.

Read the paper:

Indexing, Elaboration and Refinement: Incremental Learning of Explanatory Cases

by Ashwin Ram

Machine Learning journal, 10:201-248, 1993
www.cc.gatech.edu/faculty/ashwin/papers/git-cc-92-03.pdf

Multistrategy Learning in Reactive Control Systems for Autonomous Robotic Navigation

This paper presents a self-improving reactive control system for autonomous robotic navigation. The navigation module uses a schema-based reactive control system to perform the navigation task. The learning module combines case-based reasoning and reinforcement learning to continuously tune the navigation system through experience. The case-based reasoning component perceives and characterizes the system’s environment, retrieves an appropriate case, and uses the recommendations of the case to tune the parameters of the reactive control system. The reinforcement learning component refines the content of the cases based on the current experience. Together, the learning components perform on-line adaptation, resulting in improved performance as the reactive control system tunes itself to the environment, as well as on-line case learning, resulting in an improved library of cases that capture environmental regularities necessary to perform on-line adaptation. The system is extensively evaluated through simulation studies using several performance metrics and system configurations.

Read the paper:

Multistrategy Learning in Reactive Control Systems for Autonomous Robotic Navigation

by Ashwin Ram, Juan Carlos Santamaria

Informatica, 17(4):347-369, 1993

www.cc.gatech.edu/faculty/ashwin/papers/er-93-09.pdf