Evaluating Architectures for Intelligence

By Kaminka, Gal A.; Burghart, Catherina R. | AI Magazine, Winter 2007 | Go to article overview

Evaluating Architectures for Intelligence


Kaminka, Gal A., Burghart, Catherina R., AI Magazine


Architectures form an integral part of artificially intelligent agents and robots. Architectures structure and organize the knowledge used by the agents to select actions in dynamic environments, plan and solve problems, learn, and coordinate with others. Architectures serve to integrate general capabilities expected of an intelligent agent (for example, planning and learning), to implement and test theories about agent cognition, and to explore domain-independent mechanisms for intelligence.

As AI research has improved in formal and empirical rigor, traditional evaluation methodologies for architectures have sometimes proved insufficient. Formal analysis has often proved elusive; we seem to be missing the notation required for proving properties of architectures. Experiments that demonstrate generality are notoriously expensive to perform and are not sufficiently informative. And at a high level, evaluation is difficult because the criteria are not well defined: Is it generality? What is the ease of programmability? Is it compatible with data from biology and psychology? There are no established evaluation methodologies and only a handful of established evaluation criteria.

Recognizing that scientific progress depends on the ability to conduct informative evaluation (by experiment or formal analysis), this workshop addressed the methodologies needed for evaluating architectures. The focus was on methodology, rather than specific architectures. This workshop's goal was to propose and discuss evaluation criteria for architectures, with the hope of generating a concrete result that will initiate a real process within the community. …

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