Generality in artificial intelligence
Communications of the ACM
Incremental, instance-based learning of independent and graded concept descriptions
Proceedings of the sixth international workshop on Machine learning
Artificial economic life: a simple model of a stockmarket
Proceedings of the NATO advanced research workshop and EGS topical workshop on Chaotic advection, tracer dynamics and turbulent dispersion
Tracking Context Changes through Meta-Learning
Machine Learning - Special issue on multistrategy learning
Machine Learning - Special issue on context sensitivity and concept drift
ACM Computing Surveys (CSUR)
Local models semantics, or contextual reasoning = locality + compatibility
Artificial Intelligence
SDML: A Multi-Agent Language for Organizational Modelling
Computational & Mathematical Organization Theory
CONTEXT '01 Proceedings of the Third International and Interdisciplinary Conference on Modeling and Using Context
Robust classification with context-sensitive features
IEA/AIE'93 Proceedings of the 6th international conference on Industrial and engineering applications of artificial intelligence and expert systems
Analysis and design of physical and social contexts in multi-agent systems using UML
SELMAS '05 Proceedings of the fourth international workshop on Software engineering for large-scale multi-agent systems
Integrating learning and inference in multi-agent systems using cognitive context
MABS'06 Proceedings of the 2006 international conference on Multi-agent-based simulation VII
What is context and how can an agent learn to find and use it when making decisions?
CEEMAS'05 Proceedings of the 4th international Central and Eastern European conference on Multi-Agent Systems and Applications
Recognizing team context during simulated missions
Proceedings of the ACM 2012 conference on Computer Supported Cooperative Work
Analysis and design of physical and social contexts in multi-agent systems
Software Engineering for Multi-Agent Systems IV
MRC'05 Proceedings of the Second international conference on Modeling and Retrieval of Context
Context in social simulation: why it can't be wished away
Computational & Mathematical Organization Theory
Hi-index | 0.00 |
The use of context can considerably facilitate reasoning by restricting the beliefs reasoned upon to those relevant and providing extra information specific to the context. Despite the use and formalization of context being extensively studied both in AI and ML, context has not been much utilized in agents. This may be because many agents are only applied in a single context, and so these aspects are implicit in their design, or it may be that the need to explicitly encode information about various contexts is onerous. An algorithm to learn the appropriate context along with knowledge relevant to that context gets around these difficulties and opens the way for the exploitation of context in agent design. The algorithm is described and the agents compared with agents that learn and apply knowledge in a generic way within an artificial stock market. The potential for context as a principled manner of closely integrating crisp reasoning and fuzzy learning is discussed.