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
CYC: a large-scale investment in knowledge infrastructure
Communications of the ACM
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
Sparse Distributed Memory
Learning and exploiting context in agents
Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 3
Folk Psychology for Human Modelling: Extending the BDI Paradigm
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 1
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
Strongly typed genetic programming
Evolutionary Computation
Context in social simulation: why it can't be wished away
Computational & Mathematical Organization Theory
| Hi-index | 0.00 |
Both learning and reasoning are important aspects of intelligence. However they are rarely integrated within a single agent. Here it is suggested that imprecise learning and crisp reasoning may be coherently combined via the cognitive context. The identification of the current context is done using an imprecise learning mechanism, whilst the contents of a context are crisp models that may be usefully reasoned about. This also helps deal with situations of logical under- and overdetermination because the scope of the context can be adjusted to include more or less knowledge into the reasoning process. An example model is exhibited where an agent learns and acts in an artificial stock market.