Goal reasoning agents can solve novel problems by detecting an anomaly between expectations and observations, generating explanations about plausible causes for the anomaly, and formulating goals to remove the cause. Yet, not all anomalies represent problems. This paper addresses discerning the difference between benign anomalies and those that represent an actual problem for an agent. Furthermore, we present a new definition of the term “problem” in a goal reasoning context. This paper discusses the role of explanations and goal formulation in response to the developing problems and implements it; the paper also illustrates the above in a mine clearance domain and a labor relations domain. We also show the empirical difference between a standard planning agent, an agent that detects anomalies, and an agent that recognizes problems.
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Problem recognition, explanation and goal formulation
Goal reasoning agents can solve novel problems by detecting an anomaly between expectations and observations; generating explanations about plausible causes for the anomaly; and formulating goals to remove the cause. Yet not all anomalies represent problems. We claim that the task of discerning the difference between benign anomalies and those that represent an actual problem by an agent will increase its performance. Furthermore, we present a new definition of the term “problem” in a goal reasoning context. This paper discusses the role of explanations and goal formulation in response to developing problems and implements the response. The paper illustrates goal formulation in a mine clearance domain and a labor relations domain. We also show the empirical difference between a standard planning agent, an agent that detects anomalies and an agent that recognizes problems.
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- Award ID(s):
- 1849131
- PAR ID:
- 10347605
- Editor(s):
- Cox, Michael T.
- Date Published:
- Journal Name:
- Proceedings of the Seventh Annual Conference on Advances in Cognitive Systems
- Page Range / eLocation ID:
- 437-452
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
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