Proceedings of the 2013 Annual Conference on Advances in Cognitive Systems: Workshop on Metacognition about Artificial Situated Agents

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Metacognition is the process of thinking about thinking. It provides cognitive systems the ability to note and deal with anomalies, changes, opportunities, surprises and uncertainty. It includes both monitoring of cognitive activities and control of such activities. Monitoring helps to evaluate and explain the cognitive activities, while control helps to adapt or modify the cognitive activities. Situated agents are agents embedded in a dynamic environment that they can sense or perceive and manipulate or change through their actions. Similarly, they can act in order to manipulate other agents among which they are situated. Examples might include robots, natural language dialog interfaces, web-based agents or virtual-reality bots. An agent can leverage metacognition of its own thinking about other agents in its situated environment. It can equally benefit from metacognition of the thinking of other agents towards itself. Metacognitive monitoring can help situated agents in negotiations, conflict resolution and norm-awareness. Metacognitive control can help coordination and coalition formations of situated social agents. In this workshop, we investigate the monitoring and control aspects of metacognition about self and other agents, and their application to situated artificial agents. The papers in this report cover some of the current work related to metacognition in the areas of meta-knowledge representation, meta-reasoning and meta-cognitive architecture. Perlis et al. outlines a high-level view of architectures for real-time situated agents and the reliance of such agents on metacognition. Mbale, K. and Josyula, D. present a generic metacognitive component based on preserving the homeostasis of a host agent. Pickett, M. presents a framework for representing, learning, and processing meta-knowledge. Riddle, P. et al. discuss meta-level search through a problem representation space for problem–reformulation. Caro, M et al. use metamemory to adapt to changes in memory retrieval constraints. Langley, P. et al. abstract general problem specific abilities into strategic problem solving knowledge in an architecture for flexible problem solving across various domains. Samsonovich, A. examines metacognition as a means to improve fluid intelligence in a cognitive architecture. Perlis, D. and Cox, M. discuss the application of metacognitive monitoring to anomaly detection and goal generation.