Cumulative Learning: Towards Designing Cognitive Architectures for Artificial Agents that Have a Lifetime

Swarup, Samarth; Mahmud, M.M. Hassan; Lakkaraju, Kiran; Ray, Sylvian R.

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Description

Title

Cumulative Learning: Towards Designing Cognitive Architectures for Artificial Agents that Have a Lifetime

Author(s)

• Swarup, Samarth
• Mahmud, M.M. Hassan
• Lakkaraju, Kiran
• Ray, Sylvian R.

Issue Date

2005-02

Keyword(s)

• architecture
• artificial intelligence

Date of Ingest

2009-04-17T16:07:09Z

Abstract

Cognitive architectures should be designed with learning performance as a central goal. A critical feature of intelligence is the ability to apply the knowledge learned in one context to a new context. A cognitive agent is expected to have a lifetime, in which it has to learn to solve several different types of tasks in its environment. In such a situation, the agent should become increasingly better adapted to its environment. This means that its learning performance on each new task should improve as it is able to transfer knowledge learned in previous tasks to the solution of the new task. We call this ability cumulative learning. Cumulative learning thus refers to the accumulation of learned knowledge over a lifetime, and its application to the learning of new tasks. We believe that creating agents that exhibit sophisticated, long-term, adaptive behavior is going to require this kind of approach.

Type of Resource

text

Genre of Resource

Technical Report

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http://hdl.handle.net/2142/10961

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