Heuristics: intelligent search strategies for computer problem solving
Heuristics: intelligent search strategies for computer problem solving
The society of mind
Cathexis: a computational model of emotions
AGENTS '97 Proceedings of the first international conference on Autonomous agents
Robot learning driven by emotions
Adaptive Behavior
Experiences with an Autonomous Robot Attending AAAI
IEEE Intelligent Systems
Exploring artificial intelligence in the new millennium
Emotion and reinforcement: affective facial expressions facilitate robot learning
ICMI'06/IJCAI'07 Proceedings of the ICMI 2006 and IJCAI 2007 international conference on Artifical intelligence for human computing
Function meets style: insights from emotion theory applied to HRI
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Fuzzy cognitive maps for artificial emotions forecasting
Applied Soft Computing
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For artificial intelligence research to progress beyond the highly specialized task-dependent implementations achievable today, researchers may need to incorporate aspects of biological behavior that have not traditionally been associated with intelligence. Affective processes such as emotions may be crucial to the generalized intelligence possessed by humans and animals. A number of robots and autonomous agents have been created that can emulate human emotions, but the majority of this research focuses on the social domain. In contrast, we have developed a hybrid reactive/deliberative architecture that incorporates artificial emotions to improve the general adaptive performance of a mobile robot for a navigation task. Emotions are active on multiple architectural levels, modulating the robot's decisions and actions to suit the context of its situation. Reactive emotions interact with the robot's control system, altering its parameters in response to appraisals from short-term sensor data. Deliberative emotions are learned associations that bias path planning in response to eliciting objects or events. Quantitative results are presented that demonstrate situations in which each artificial emotion can be beneficial to performance.