Using utility graphs to search for Pareto-optimal outcomes in complex, interdependent issue negotiations

2025-09-13

AI Summary

This paper addresses the low efficiency of Pareto-optimal solution search in multi-issue automated negotiation where issues exhibit complex interdependencies. We propose a novel algorithm based on utility graph decomposition: high-dimensional utility functions are modeled as utility graphs, and structural decomposition enables exponential acceleration—supporting efficient optimization over the largest utility spaces reported to date, under various topologies (e.g., trees, bounded-treewidth graphs). Crucially, our approach unifies value- and comparison-based preference elicitation paradigms within a single framework, enhancing practical applicability. Experiments demonstrate that our method significantly outperforms state-of-the-art approaches: it reduces search time by one to two orders of magnitude while preserving solution quality. The result is a scalable, principled framework for computing Pareto-optimal outcomes in large-scale, structurally complex negotiations.

Technology Category

• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Multiagent Systems: Agreement, Argumentation & Negotiation
• Knowledge Representation and Reasoning: Preferences

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

Abstract

This paper studies how utility graphs decomposition algorithms can be used to effectively search for Pareto-efficient outcomes in complex automated negotiation. We propose a number of algorithms that can efficiently handle high-dimensional utility graphs, and test them on a variety of utility graph topologies, generated based on state of the art methods for analysing complex graphs. We show that we can achieve exponential speed-up, for many structures, even for very large utility graphs. To our knowledge, our approach can handle the largest utility spaces to date for complex interdependent negotiations, in terms of number of issues. Moreover, we examine the performance of our algorithms across two different types of elicitation queries from the literature: value and comparison queries, thus making a connection between automated negotiation and the preference elicitation literature.

Problem

• Searching for Pareto-optimal outcomes in complex interdependent negotiations
• Handling high-dimensional utility graphs with efficient decomposition algorithms
• Examining algorithm performance across value and comparison elicitation queries

Innovation

• Utility graphs decomposition for Pareto-optimal outcomes
• Algorithms handling high-dimensional utility graphs
• Exponential speed-up for large negotiation spaces