Introduction to operations research, 4th ed.
Introduction to operations research, 4th ed.
Combinatorial optimization: algorithms and complexity
Combinatorial optimization: algorithms and complexity
Partial constraint satisfaction
Artificial Intelligence - Special volume on constraint-based reasoning
Semiring-based constraint satisfaction and optimization
Journal of the ACM (JACM)
Strategic negotiation in multiagent environments
Strategic negotiation in multiagent environments
Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 2
Complexity and Approximation: Combinatorial Optimization Problems and Their Approximability Properties
Algorithms for Distributed Constraint Satisfaction: A Review
Autonomous Agents and Multi-Agent Systems
Dialogues for Negotiation: Agent Varieties and Dialogue Sequences
ATAL '01 Revised Papers from the 8th International Workshop on Intelligent Agents VIII
Artificial Intelligence: A Modern Approach
Artificial Intelligence: A Modern Approach
Optimal agendas for multi-issue negotiation
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
Scientific approaches and techniques for negotiation. A game theoretic and artificial intelligence perspective
Argumentation-based negotiation
The Knowledge Engineering Review
Negotiating over small bundles of resources
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
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Allocation of indivisible goods: a general model and some complexity results
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
Automated theorem proving: A logical basis (Fundamental studies in computer science)
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Propositional-logic approach to one-shot multi issue bilateral negotiation
ACM SIGecom Exchanges
Hard and soft constraints for reasoning about qualitative conditional preferences
Journal of Heuristics
A logic-based framework to compute Pareto agreements in one-shot bilateral negotiation
Proceedings of the 2006 conference on ECAI 2006: 17th European Conference on Artificial Intelligence August 29 -- September 1, 2006, Riva del Garda, Italy
A computational model of logic-based negotiation
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Journal of Artificial Intelligence Research
A modal semantics for an argumentation-based pragmatics for agent communication
ArgMAS'04 Proceedings of the First international conference on Argumentation in Multi-Agent Systems
Configuring software product line feature models based on Stakeholders' soft and hard requirements
SPLC'10 Proceedings of the 14th international conference on Software product lines: going beyond
KEMNAD: A Knowledge Engineering Methodology For Negotiating Agent Development
Computational Intelligence
Computers and Industrial Engineering
Semantic matchmaking and ranking: beyond deduction in retrieval scenarios
RR'12 Proceedings of the 6th international conference on Web Reasoning and Rule Systems
An efficient automated negotiation strategy for complex environments
Engineering Applications of Artificial Intelligence
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We present a novel logic-based framework to automate multi-issue bilateral negotiation in e-commerce settings. The approach exploits logic as communication language among agents, and optimization techniques in order to find Pareto-efficient agreements. We introduce $${\mathcal{P}}({\mathcal{N}})$$ , a propositional logic extended with concrete domains, which allows one to model relations among issues (both numerical and non-numerical ones) via logical entailment, differently from well-known approaches that describe issues as uncorrelated. Through $${\mathcal{P}}({\mathcal{N}})$$ it is possible to represent buyer's request, seller's supply and their respective preferences as formulas endowed with a formal semantics, e.g., "if I spend more than 30000 驴 for a sedan then I want more than a two-years warranty and a GPS system included". We mix logic and utility theory in order to express preferences in a qualitative and quantitative way. We illustrate the theoretical framework, the logical language, the one-shot negotiation protocol we adopt, and show we are able to compute Pareto-efficient outcomes, using a mediator to solve an optimization problem. We prove the computational adequacy of our method by studying the complexity of the problem of finding Pareto-efficient solutions in our setting.