Much research in artificial intelligence is concerned with the development of autonomous
agents that can interact effectively with other agents. An important aspect of such agents is …

A negotiation between agents is typically an incomplete information game, where the agents
initially do not know their opponent's preferences or strategy. This poses a challenge, as …

The rapidly growing field of computational social choice, at the intersection of computer
science and economics, deals with the computational aspects of collective decision making …

Abstract Majority of the Multiple-Attribute Decision Making (MADM) methods assume that the
criteria are independent of each other, which is not a realistic assumption in many real world …

Constraint programming is a powerful paradigm for solving combinatorial search problems
that draws on a wide range of techniques from artificial intelligence, computer science …

This chapter provides an overview of recent work on preference learning and ranking via
pairwise classification. The learning by pairwise comparison (LPC) paradigm is the natural …

Abstract We introduce PrefLib: A Library for Preferences; an online resource located at
http://www. preflib. org. With the emergence of computational social choice and an increased …

Preference learning is an emerging topic that appears in different guises in the recent
literature. This work focuses on a particular learning scenario called label ranking, where the …

Given an image, we propose a hierarchical generative model that classifies the overall
scene, recognizes and segments each object component, as well as annotates the image …

Issues in Multiagent Resource Allocation Yann Chevaleyre, Paul E. Dunne, Ulle Endriss,
Jerome Lang, Michel Lemaitre, Nicolas Maudet, Julian Padget, Steve Phelps, Juan A …