Partial preferences and ambiguity resolution in contextual defeasible logic
LPNMR'11 Proceedings of the 11th international conference on Logic programming and nonmonotonic reasoning
Human-Centered planning for adaptive user situation in ambient intelligence environment
PRIMA'11 Proceedings of the 14th international conference on Agents in Principle, Agents in Practice
MIWAI'11 Proceedings of the 5th international conference on Multi-Disciplinary Trends in Artificial Intelligence
Forgetting for defeasible logic
LPAR'12 Proceedings of the 18th international conference on Logic for Programming, Artificial Intelligence, and Reasoning
Defeasible argumentation for multi-agent planning in ambient intelligence applications
Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
Context-Aware Multi-Agent Planning in intelligent environments
Information Sciences: an International Journal
DEAL: A Distributed Authorization Language for Ambient Intelligence
International Journal of Ambient Computing and Intelligence
A formal approach to model the interaction between user and ami environment
ICPCA/SWS'12 Proceedings of the 2012 international conference on Pervasive Computing and the Networked World
Automata for infinite argumentation structures
Artificial Intelligence
A rule-based contextual reasoning platform for ambient intelligence environments
RuleML'13 Proceedings of the 7th international conference on Theory, Practice, and Applications of Rules on the Web
| Hi-index | 0.00 |
The imperfect nature of context in Ambient Intelligence environments and the special characteristics of the entities that possess and share the available context information render contextual reasoning a very challenging task. The accomplishment of this task requires formal models that handle the involved entities as autonomous logic-based agents and provide methods for handling the imperfect and distributed nature of context. This paper proposes a solution based on the Multi-Context Systems paradigm in which local context knowledge of ambient agents is encoded in rule theories (contexts), and information flow between agents is achieved through mapping rules that associate concepts used by different contexts. To handle imperfect context, we extend Multi-Context Systems with nonmonotonic features: local defeasible theories, defeasible mapping rules, and a preference ordering on the system contexts. On top of this model, we have developed an argumentation framework that exploits context and preference information to resolve potential conflicts caused by the interaction of ambient agents through the mappings, and a distributed algorithm for query evaluation.