Social conceptions of knowledge and action: DAI foundations and open systems semantics
Artificial Intelligence
Intelligence without representation
Artificial Intelligence
Dyna, an integrated architecture for learning, planning, and reacting
ACM SIGART Bulletin
Simulating organizations: computational models of institutions and groups
Simulating organizations: computational models of institutions and groups
Learning, action and consciousness: a hybrid approach toward modelling consciousness
Neural Networks - 1997 special issue on neural networks for consciousness
Modelling social action for AI agents
Artificial Intelligence - Special issue: artificial intelligence 40 years later
Some experiments with a hybrid model for learning sequential decision making
Information Sciences—Informatics and Computer Science: An International Journal
Artificial Societies: The Computer Simulation of Social Life
Artificial Societies: The Computer Simulation of Social Life
Simulating with Cognitive Agents: The Importance of Cognitive Emergence
Proceedings of the First International Workshop on Multi-Agent Systems and Agent-Based Simulation
Emergence and Cognition: Towards a Synthetic Paradigm in AI and Cognitive Science
IBERAMIA '98 Proceedings of the 6th Ibero-American Conference on AI: Progress in Artificial Intelligence
Adaptive Selection of Reactive/Deliberate Planning for the Dynamic Environment
Proceedings of the 8th European Workshop on Modelling Autonomous Agents in a Multi-Agent World: Multi-Agent Rationality
AAAI'90 Proceedings of the eighth National conference on Artificial intelligence - Volume 1
Towards a Methodology for Experiments with Autonomous Agents
SBIA '02 Proceedings of the 16th Brazilian Symposium on Artificial Intelligence: Advances in Artificial Intelligence
A system of exchange values to support social interactions in artificial societies
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
Modeling one human decision maker with a multi-agent system: the CODAGE approach
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
Simulating human intuitive decisions by Q-learning
Proceedings of the 2009 ACM symposium on Applied Computing
Dynamics of Rule Revision and Strategy Revision in Legislative Games
Proceedings of the 2005 conference on Legal Knowledge and Information Systems: JURIX 2005: The Eighteenth Annual Conference
Cognitive simulation of academic science
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Concepts in complexity engineering
International Journal of Bio-Inspired Computation
Applied Ontology - The Ontology of Functions
ELMS: an environment description language for multi-agent simulation
E4MAS'04 Proceedings of the First international conference on Environments for Multi-Agent Systems
Dealing with Interaction for Complex Systems Modelling and Prediction
International Journal of Artificial Life Research
Hi-index | 0.00 |
A basic claim of this paper is that the foundational theoretical problem of the social sciences - the possibility of unconscious, unplanned forms of cooperation and intelligence among intentional agents (the very hard issue of the 'invisible hand', of the 'spontaneous social order' but also of 'social functions') - will eventually be clarified thanks to the contribution of AI (and, in particular, of cognitive Agent modelling, learning, and MAS) and its entering the social simulation domain. After introducing Multi-Agent-Based Social Simulation and its trends, the limits of the very popular notion of 'emergence' are discussed, Smith's and Hayek's view of 'spontaneous social order' are critically introduced, and serious contradictions in the theory of 'social functions' among intentional agents are pointed out. The problem is how to reconcile the 'external' teleology that orients the agent's behaviour with the 'internal' teleology governing it. In order to account for the functional character of intentional action, we need a somewhat sophisticated model of intention, and a different view of layered cognitive architectures combining explicit beliefs and goals with association and conditioning. On such a basis we sketch a model of unknown functions impinging on intentional actions through a high level form of (MA) reinforcement learning. This model accounts for both eu-functions and dys-functions, autonomous and heteronomous functions. It is argued that, in order to reproduce some behaviour, its effects should not necessarily be 'good', i.e. useful for the goal of the agent or of some higher macro-system.