Knowlege in action: logical foundations for specifying and implementing dynamical systems
Knowlege in action: logical foundations for specifying and implementing dynamical systems
Mind as Motion: Explorations in the Dynamics of Cognition
Mind as Motion: Explorations in the Dynamics of Cognition
Dynamics of a Classical Conditioning Model
Autonomous Robots
LEADSTO: a language and environment for analysis of dynamics by simulation
MATES'05 Proceedings of the Third German conference on Multiagent System Technologies
Formalisation and analysis of the temporal dynamics of conditioning
AOSE'05 Proceedings of the 6th international conference on Agent-Oriented Software Engineering
Representation in dynamical and embodied cognition
Cognitive Systems Research
A temporal modelling environment for internally grounded beliefs, desires and intentions
Cognitive Systems Research
Specification and Verification of Dynamics in Cognitive Agent Models
IAT '06 Proceedings of the IEEE/WIC/ACM international conference on Intelligent Agent Technology
Cognitive and social simulation of criminal behaviour: the intermittent explosive disorder case
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
| Hi-index | 0.00 |
To simulate adaptive agents with abilities matching those of their real-world counterparts, a natural approach is to incorporate adaptation mechanisms such as classical conditioning into agent-based simulation. Existing models for adaptation mechanisms are usually based on quantitative methods such as DST. In contrast, agent-based simulation is usually based on qualitative, logical languages. To bridge this gap, this paper puts forward an integrative approach to simulate and analyse the conditioning process of an adaptive agent, which integrates quantitative and qualitative aspects within one temporal specification language. The approach comprises (1) simulation of adaptation mechanisms in an executable language, (2) automated analysis of dynamic properties against simulation traces, and (3) verification of representation relations for internal agent states against simulation traces. Furthermore, the approach addresses the issue of realism of intermediate states in a simulated conditioning process.