Automatic programming of behavior-based robots using reinforcement learning
Artificial Intelligence
The affective reasoner: a process model of emotions in a multi-agent system
The affective reasoner: a process model of emotions in a multi-agent system
Reinforcement learning for robots using neural networks
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Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
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ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
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IEEE Transactions on Neural Networks
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ACM Transactions on Graphics (TOG)
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Lecture Notes in Computer Science
Emotional Behavior: A Resource Management Approach
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
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Proposal for a Multiagent Architecture for Self-Organizing Systems (MA-SOS)
PAISI, PACCF and SOCO '08 Proceedings of the IEEE ISI 2008 PAISI, PACCF, and SOCO international workshops on Intelligence and Security Informatics
Artificial emotions: are we ready for them?
ECAL'07 Proceedings of the 9th European conference on Advances in artificial life
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Journal of Ambient Intelligence and Smart Environments
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Expert Systems with Applications: An International Journal
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ICMI'06/IJCAI'07 Proceedings of the ICMI 2006 and IJCAI 2007 international conference on Artifical intelligence for human computing
MIV'05 Proceedings of the 5th WSEAS international conference on Multimedia, internet & video technologies
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EPIA'05 Proceedings of the 12th Portuguese conference on Progress in Artificial Intelligence
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ICVS'05 Proceedings of the Third international conference on Virtual Storytelling: using virtual reality technologies for storytelling
OpenPsi: A novel computational affective model and its application in video games
Engineering Applications of Artificial Intelligence
Intelligent virtual humans with autonomy and personality: State-of-the-art
Intelligent Decision Technologies
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The existence of emotion and cognition as two interacting systems, both with important roles in decision-making, has been recently advocated by neurophysiological research (LeDoux, 1998, Damasio, 1994. Following that idea, this paper presents the ALEC agent architecture which has both emotive and cognitive learning, as well as emotive and cognitive decision-making capabilities to adapt to real-world environments. These two learning mechanisms embody very different properties which can be related to those of natural emotion and cognition systems. The reported experiments test ALEC within a simulated autonomous robot which learns to perform a multi-goal and multi-step survival task when faced with real world conditions, namely continuous time and space, noisy sensors and unreliable actuators. Experimental results show that both systems contribute positively to the learning performance of the agent.