Posts Tagged ‘case-based reasoning’

27 Jun

Knowledge Compilation and Speedup Learning in Continuous Task Domains

Posted by cognitivecomputing in Learning, Robotics. Tagged: , .

Many techniques for speedup learning and knowledge compilation focus on the learning and optimization of macro-operators or control rules in task domains that can be characterized using a problem-space search paradigm. However, such a characterization does not fit well the class of task domains in which the problem solver is required to perform in a continuous manner. For example, in many robotic domains, the problem solver is required to monitor real-valued perceptual inputs and vary its motor control parameters in a continuous, on-line manner to successfully accomplish its task. In such domains, discrete symbolic states and operators are difficult to define.

To improve its performance in continuous problem domains, a problem solver must learn, modify, and use “continuous operators” that continuously map input sensory information to appropriate control outputs. Additionally, the problem solver must learn the contexts in which those continuous operators are applicable. We propose a learning method that can compile sensorimotor experiences into continuous operators, which can then be used to improve performance of the problem solver. The method speeds up the task performance as well as results in improvements in the quality of the resulting solutions. The method is implemented in a robotic navigation system, which is evaluated through extensive experimentation.

Read the paper:

Knowledge Compilation and Speedup Learning in Continuous Task Domains

by Juan Carlos Santamaria, Ashwin Ram

ICML-93 Workshop on Knowledge Compilation and Speedup Learning, Amherst, MA, June 1993
www.cc.gatech.edu/faculty/ashwin/papers/er-93-07.pdf

1 Jun

Creative Conceptual Change

Posted by cognitivecomputing in Language, Learning, Robotics, Talks. Tagged: , , , , .

Creative conceptual change involves (a) the construction of new concepts and of coherent belief systems, or theories, relating these concepts, and (b) the modification and extrapolation of existing concepts and theories in novel situations. The first kind of process involves reformulating perceptual, sensorimotor, or other low-level information into higher-level abstractions. The second kind of process involves a temporary suspension of disbelieve and the extension or adaptation of existing concepts to create a conceptual model of a new situation which may be very different from previous real-world experience.

We discuss these and other types of conceptual change, and present computational models of constructive and extrapolative processes in creative conceptual change. The models have been implemented as computer programs in two very different “everyday” task domains: (a) SINS is an autonomous robotic navigation system that learns to navigate in an obstacle-ridden world by constructing sensorimotor concepts that represent navigational strategies, and (b) ISAAC is a natural language understanding system that reads short stories from the science fiction genre which requires a deep understanding of concepts that might be very different from the concepts that the system is familiar with.

Read the paper:

Creative Conceptual Change

by Ashwin Ram, Kenneth Moorman, Juan Carlos Santamaria

Invited talk at the 15th Annual Conference of the Cognitive Science Society, Boulder, CO, June 1993. Long version published as Technical Report GIT-CC-96/07, College of Computing, Georgia Institute of Technology, Atlanta, GA, 1996. 
www.cc.gatech.edu/faculty/ashwin/papers/er-93-04.pdf

1 Jan

Indexing, Elaboration and Refinement: Incremental Learning of Explanatory Cases

Posted by cognitivecomputing in Learning. Tagged: , , , .

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

1 Jan

Multistrategy Learning in Reactive Control Systems for Autonomous Robotic Navigation

Posted by cognitivecomputing in Learning, Robotics. Tagged: , , .

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

1 Apr

Learning Indices for Schema Selection

Posted by cognitivecomputing in Learning. Tagged: , .

In addition to learning new knowledge, a system must be able to learn when the knowledge is likely to be applicable. An index is a piece of information which, when identified in a given situation, triggers the relevant piece of knowledge (or schema) in the system’s memory. We discuss the issue of how indices may be learned automatically in the context of a story understanding task, and present a program that can learn new indices for existing explanatory schemas. We discuss two methods using which the system can identify the relevant schema even if the input does not directly match an existing index, and learn a new index to allow it to retrieve this schema more efficiently in the future.

Read the paper:

Learning Indices for Schema Selection

by Sam Bhatta, Ashwin Ram

Florida Artificial Intelligence Research Symposium (FLAIRS-91), 226-231, Cocoa Beach, FL, April 1991
www.cc.gatech.edu/faculty/ashwin/papers/er-91-01.pdf

27 Jul

Decision Models: A Theory of Volitional Explanation

Posted by cognitivecomputing in Language. Tagged: , , , , .

This paper presents a theory of motivational analysis, the construction of volitional explanations to describe the planning behavior of agents. We discuss both the content of such explanations as well as the process by which an understander builds the explanations. Explanations are constructed from decision models, which describe the planning process that an agent goes through when considering whether to perform an action. Decision models are represented as explanations patterns, which are standard patterns of causality based on previous experiences of the understander. We discuss the nature of explanation patterns, their use in representing decision models, and the process by which they are retrieved, used, and evaluated.

Read the paper:

Decision Models: A Theory of Volitional Explanation

by Ashwin Ram

Twelvth Annual Conference of the Cognitive Science Society (CogSci-90), Cambridge, MA, July 1990
www.cc.gatech.edu/faculty/ashwin/papers/er-90-03.pdf
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