The Complexity of Optimal Queuing Network Control
Mathematics of Operations Research
Agents with or without Emotions?
Proceedings of the Fifteenth International Florida Artificial Intelligence Research Society Conference
Establishing and maintaining long-term human-computer relationships
ACM Transactions on Computer-Human Interaction (TOCHI)
The Emotion Machine: Commonsense Thinking, Artificial Intelligence, and the Future of the Human Mind
The Emotion Machine: Commonsense Thinking, Artificial Intelligence, and the Future of the Human Mind
Empirically building and evaluating a probabilistic model of user affect
User Modeling and User-Adapted Interaction
Expression of Moral Emotions in Cooperating Agents
IVA '09 Proceedings of the 9th International Conference on Intelligent Virtual Agents
Approximation algorithms for restless bandit problems
Journal of the ACM (JACM)
EMA: A process model of appraisal dynamics
Cognitive Systems Research
Irrationality in persuasive argumentation
Logic Programs, Norms and Action
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We present a novel methodology for decision-making by computer agents that leverages a computational concept of emotions. It is believed that emotions help living organisms perform well in complex environments. Can we use them to improve the decision-making performance of computer agents? We explore this possibility by formulating emotions as mathematical operators that serve to update the relative priorities of the agent's goals. The agent uses rudimentary domain knowledge to monitor the expectation that its goals are going to be accomplished in the future, and reacts to changes in this expectation by "experiencing emotions." The end result is a projection of the agent's long-run utility function, which might be too complex to optimize or even represent, to a time-varying valuation function that is being myopically maximized by selecting appropriate actions. Our methodology provides a systematic way to incorporate emotion into a decision-theoretic framework, and also provides a principled, domain-independent methodology for generating heuristics in novel situations. We test our agents in simulation in two domains: restless bandits and a simple foraging environment. Our results indicate that emotion-based agents outperform other reasonable heuristics for such difficult domains, and closely approach computationally expensive near-optimal solutions, whenever these are computable, yet requiring only a fraction of the cost.