Commonsense reasoning about causality: deriving behavior from structure
Artificial Intelligence - Special volume on qualitative reasoning about physical systems
Higher-order derivative constraints in qualitative simulation
Artificial Intelligence - Special issue: Qualitative reasoning about physical systems II
Design visual thinking tools for mixed initiative systems
Proceedings of the 7th international conference on Intelligent user interfaces
Negotiation coalitions in group-choice multi-agent systems
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
Static and Dynamic Coalition Formation in Group-Choice Decision Making
MDAI '07 Proceedings of the 4th international conference on Modeling Decisions for Artificial Intelligence
An agent negotiation engine for collaborative decision making
MDAI'06 Proceedings of the Third international conference on Modeling Decisions for Artificial Intelligence
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In Multi-Criteria Decision Making problems such as choosing a development policy, selecting software products, or searching for commodities to purchase, it is often necessary to evaluate solution options in respect of multiple objectives. The solution alternative that performs best in all the objectives is the dominant solution, and it should be selected to solve the problem. However, usually the selection objectives are incomparable and conflicting, making it impossible to have a dominant solution among the alternatives. In such cases, tradeoff analysis is required to identify the objectives that can be optimized, and those that can be comprised in order to choose a winning solution. In this paper we present a tradeoff analysis model based on the principles of qualitative reasoning that provides visualization support for understanding interaction and tradeoff dependences among solutions evaluation criteria which affect the tradeoff among selection objectives. Moreover, the decision support system based on our tradeoff analysis model facilitates discovery of hidden solution features so as to improve the completeness and certainty of the user preference model.