The Use of Background Knowledge in Decision Tree Induction
Machine Learning
Efficient incremental induction of decision trees
Machine Learning
Using Model Trees for Classification
Machine Learning
Causality: models, reasoning, and inference
Causality: models, reasoning, and inference
Machine Learning
C4.5: Programs for Machine Learning
C4.5: Programs for Machine Learning
Games That Agents Play: A Formal Framework for Dialogues between Autonomous Agents
Journal of Logic, Language and Information
Using Background Knowledge to Improve Inductive Learning: A Case Study in Molecular Biology
IEEE Expert: Intelligent Systems and Their Applications
Machine Learning
The Role of Domain Knowledge in a Large Scale Data Mining Project
SETN '02 Proceedings of the Second Hellenic Conference on AI: Methods and Applications of Artificial Intelligence
Artificial Intelligence: A Modern Approach
Artificial Intelligence: A Modern Approach
Towards interest-based negotiation
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
TRAVOS: Trust and Reputation in the Context of Inaccurate Information Sources
Autonomous Agents and Multi-Agent Systems
An integrated trust and reputation model for open multi-agent systems
Autonomous Agents and Multi-Agent Systems
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Pellet: A practical OWL-DL reasoner
Web Semantics: Science, Services and Agents on the World Wide Web
A Semantic Web Primer, 2nd Edition (Cooperative Information Systems)
A Semantic Web Primer, 2nd Edition (Cooperative Information Systems)
Collaborative plans for group activities
IJCAI'93 Proceedings of the 13th international joint conference on Artifical intelligence - Volume 1
Artificial Intelligence
Foundations of Semantic Web Technologies
Foundations of Semantic Web Technologies
Resource use pattern analysis for predicting resource availability in opportunistic grids
Concurrency and Computation: Practice & Experience - Advanced Scheduling Strategies and Grid Programming Environments
Analyzing Team Decision-Making in Tactical Scenarios
The Computer Journal
Agent Support for Policy-Driven Collaborative Mission Planning
The Computer Journal
OWL-POLAR: semantic policies for agent reasoning
ISWC'10 Proceedings of the 9th international semantic web conference on The semantic web - Volume Part I
Argumentation strategies for plan resourcing
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 3
On the benefits of argumentation-derived evidence in learning policies
ArgMAS'10 Proceedings of the 7th international conference on Argumentation in Multi-Agent Systems
Minerva: Sequential Covering for Rule Extraction
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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How do I choose whom to delegate a task to? This is an important question for an autonomous agent collaborating with others to solve a problem. Were similar proposals accepted from similar agents in similar circumstances? What arguments were most convincing? What are the costs incurred in putting certain arguments forward? Can I exploit domain knowledge to improve the outcome of delegation decisions? In this paper, we present an agent decision-making mechanism where models of other agents are refined through evidence from past dialogues and domain knowledge, and where these models are used to guide future delegation decisions. Our approach combines ontological reasoning, argumentation and machine learning in a novel way, which exploits decision theory for guiding argumentation strategies. Using our approach, intelligent agents can autonomously reason about the restrictions (e.g., policies/norms) that others are operating with, and make informed decisions about whom to delegate a task to. In a set of experiments, we demonstrate the utility of this novel combination of techniques. Our empirical evaluation shows that decision-theory, machine learning and ontology reasoning techniques can significantly improve dialogical outcomes.