Personality Detection Using Context Based Emotions in Cognitive Agents

AbstractMercier and Sperber (M&S) suggest that human reasoning is reflective and has evolved to support social interaction. Cognitive agents benefit from being able to reflect on their beliefs whether they are acting alone or socially. A formal framework for argumentation that has emerged from research on artificial cognitive systems that parallels M&S's proposals may shed light on mental processes that underpin social interactions.

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A Partially Observable Markov Decision Process-Based Blackboard Architecture for Cognitive Agents in Partially Observable Environments

IEEE Transactions on Cognitive and Developmental Systems ◽

10.1109/tcds.2020.3034428 ◽

2020 ◽

pp. 1-1

Author(s):

Hideaki Itoh ◽

Hidehiko Nakano ◽

Ryota Tokushima ◽

Hisao Fukumoto ◽

Hiroshi Wakuya

Keyword(s):

Markov Decision Process ◽

Decision Process ◽

Cognitive Agents ◽

Blackboard Architecture ◽

Markov Decision ◽

Partially Observable Markov ◽

Partially Observable

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Complex Systems Approaches to Emergent Goal Formation in Cognitive Agents

The 2006 IEEE International Joint Conference on Neural Network Proceedings ◽

10.1109/ijcnn.2006.1716790 ◽

2006 ◽

Author(s):

D. Harter

Keyword(s):

Complex Systems ◽

Cognitive Agents ◽

Systems Approaches ◽

Goal Formation

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An executive system for cognitive agents

2008 IEEE/RSJ International Conference on Intelligent Robots and Systems ◽

10.1109/iros.2008.4651207 ◽

2008 ◽

Cited By ~ 2

Author(s):

S. Kasderidis

Keyword(s):

Cognitive Agents ◽

Executive System

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Simulating civilization change with Nexus cognitive agents

Advances in Design for Cross-Cultural Activities Part I ◽

10.1201/b12316-20 ◽

2012 ◽

pp. 166-175

Keyword(s):

Cognitive Agents

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Q-Learning of Spatial Actions for Hierarchical Planner of Cognitive Agents

Lecture Notes in Computer Science - Interactive Collaborative Robotics ◽

10.1007/978-3-030-60337-3_16 ◽

2020 ◽

pp. 160-169

Author(s):

Gleb Kiselev ◽

Aleksandr Panov

Keyword(s):

Cognitive Agents ◽

Q Learning

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INCA: A cognitive multi-agents architecture for designing intelligent & adaptive learning systems

Computer Science and Information Systems ◽

10.2298/csis0502099n ◽

2005 ◽

Vol 2 (2) ◽

pp. 99-114 ◽

Cited By ~ 7

Author(s):

Thierry Nabeth ◽

Liana Razmerita ◽

Albert Angehrn ◽

Claudia Roda

Keyword(s):

Knowledge Sharing ◽

Adaptive Learning ◽

Management Practices ◽

Learning Systems ◽

Learning System ◽

Cognitive Agents ◽

E Learning ◽

Personal Agent ◽

Adaptive Learning Systems ◽

Multi Agents

This paper presents a cognitive multi-agents architecture called Intelligent Cognitive Agents (InCA) that was elaborated for the design of Intelligent Adaptive Learning Systems. The InCA architecture relies on a personal agent that is aware of the user's characteristics, and that coordinates the intervention of a set of expert cognitive agents (such as story telling agents, assessment agents, stimulation agents or help agents). This InCA architecture has been applied for the design of K"InCA, an e-learning system aimed at helping people to learn and adopt knowledge-sharing management practices.

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