The adaptive power of affect: learning in the SESAME architecture
Proceedings of the second international conference on From animals to animats 2 : simulation of adaptive behavior: simulation of adaptive behavior
A motivational system for regulating human-robot interaction
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Soar Papers: Research on Integrated Intelligence
Soar Papers: Research on Integrated Intelligence
Behavior arbitration for autonomous mobile robots using emotion mechanisms
IROS '95 Proceedings of the International Conference on Intelligent Robots and Systems-Volume 3 - Volume 3
Mechanisms of Action Selection: Introduction to the Special Issue
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
Useful roles of emotions in artificial agents: a case study from artificial life
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Emotions as durative dynamic state for action selection
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Function meets style: insights from emotion theory applied to HRI
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
A domain-independent framework for modeling emotion
Cognitive Systems Research
Human-inspired model for norm compliance decision making
Information Sciences: an International Journal
Hi-index | 0.00 |
Emotion mechanisms are often used in artificial agents as a method of improving action selection. Comparisons between agents are difficult due to a lack of unity between the theories of emotion, tasks of agents and types of action selection utilised. A set of architectural qualities is proposed as a basis for making comparisons between agents. An analysis of existing agent architectures that include an emotion mechanism can help to triangulate design possibilities within the space outlined by these qualities. With this in mind, twelve autonomous agents incorporating an emotion mechanism into action selection are selected for analysis. Each agent is dissected using these architectural qualities (the agent architecture, the action selection mechanism, the emotion mechanism and emotion state representation, along with the emotion model it is based on). This helps to place the agents within an architectural space, highlights contrasting methods of implementing similar theoretical components, and suggests which architectural aspects are important to performance of tasks. An initial framework is introduced, consisting of a series of recommendations for designing emotion mechanisms within artificial agents, based on correlations between emotion roles performed and the aspects of emotion mechanisms used to perform those roles. The conclusion discusses how problems with this type of research can be resolved and to what extent development of a framework can aid future research.