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From employment contracts to climate agreements, individuals often engage in groups that must reach decisions with varying levels of fairness. These dilemmas also pervade AI, e.g. in automated negotiation, conflict resolution or resource allocation. As evidenced by the Ultimatum Game, payoff maximization is frequently at odds with fairness. Eliciting equality in populations of self-regarding agents thus requires judicious interventions. Here we use knowledge about agents’ social networks to implement fairness mechanisms, in the context of Multiplayer Ultimatum Games. We show that preferentially attributing the role of Proposer to low-connected nodes enhances fairness. We further show that, when high-degree must be the Proposers, stricter voting rules (i.e., requiring consensus for collectives to accept a proposal) reduce unfairness.

AAMAS 2021

Eliciting Fairness in Multiplayer Bargaining through Network-Based Role Assignment

AAMAS is the largest and most influential conference in the area of agents and multiagent systems, bringing together researchers and practitioners in all areas of agent technology and providing and internationally renowned high-profile forum for publishing and finding out about the latest developments in the field. AAMAS is the flagship conference of the non-profit International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS).  

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poster

Institutions and investors face the constant challenge of making accurate decisions and predictions regarding how best they should distribute their endowments. The problem of achieving an optimal outcome at minimal cost has been extensively studied and resolved using several heuristics. However, these works fail to address how an external decision maker can target different types of fair behaviour and how limited information can shape this complex interplay. Here, we consider the well-known Ultimatum game in a spatial setting and propose a hierarchy of interference mechanisms based on the amount of information available to the external decision maker and desired standards of fairness. Our key findings show that asymmetric interactions have drastically different dynamics when compared to symmetric games, such as the Prisoner's Dilemma, and discuss why gathering information about the agents' behaviour allows for the most efficient investment strategies.

Promoting Fair Proposers, Fair Responders or Both? Cost-Efficient Interference in the Spatial Ultimatum Game

We tackle the problem of allocating teams to tasks. For that, we propose a novel allocation problem that matches teams with tasks based on the similarity between the competencies required by the tasks and the competencies offered by teams. We formally cast our problem as an optimisation problem, characterise the size of its search space and outline a heuristic approach to solve it.

Towards a Competence-Based Approach to Allocate Teams to Tasks

Liquid Democracy (LD) uses transitive delegations in voting. In its simplest form, it is used for binary decisions, however its promise holds also for more advanced voting settings. Here we consider LD in the context of Participatory Budgeting (PB), which is a direct democracy approach to budgeting, most usually done in municipal budgeting processes. In particular, we study Knapsack Voting, in which PB voters approve projects, such that the sum of costs of projects each voter approves must respect the budget limit. We observe possible inconsistencies, as the cost of voter-approved projects may go over the budget limit after resolving delegations. We offer ways to overcome them by studying the computational complexity of updating as few delegations as possible to arrive  after following all project delegations  to a consistent profile.

Preserving Consistency for Liquid Knapsack Voting

Search has played a fundamental role in computer game research since the very beginning.   And while online search has been commonly used in perfect information games such as Chess and Go, online search methods for imperfect information games have only been introduced relatively recently. This paper addresses the question of what is a sound online algorithm in an imperfect information setting of two-player zero-sum games? We argue that the fixed-strategy definitions of exploitability and epsilon-Nash equilibria are ill suited to measure the worst-case performance of an online algorithm. We thus formalize epsilon-soundness, a concept that connects the worst-case performance of an online algorithm to the performance of an epsilon-Nash equilibrium. Our definition of soundness and the consistency hierarchy finally provide appropriate tools to analyze online algorithms in repeated imperfect information games.  We thus inspect some of the previous online algorithms in a new light, bringing new insights into their worst case performance guarantees.

Sound Algorithms in Imperfect Information Games

In robotics, it is often not possible to learn useful policies using pure model-free reinforcement learning without significant reward shaping or  curriculum learning. As a consequence, many researchers rely on expert demonstrations to guide learning. However, acquiring expert  demonstrations can be expensive. This paper proposes an alternative approach where the solutions of previously solved tasks are used to produce an action prior that can facilitate exploration in future tasks. The action prior is a probability distribution over actions that summarizes the set of policies found solving previous tasks. Our results indicate that this approach can be used to solve robotic manipulation problems that would otherwise be infeasible without expert demonstrations. Source code is available at https://github.com/ondrejba/action_priors.

Action Priors for Large Action Spaces in Robotics

Conditional preference statements have been used to compactly represent preferences over combinatorial domains. They are at the core of CP-nets and their generalizations, and lexicographic preference trees. Several works have addressed the complexity of some queries (optimization, dominance in particular). We extend in this paper some of these results, and study other queries which have not been addressed so far, like equivalence, thereby contributing to a knowledge compilation map for languages bas­ed on conditional preference statements. We also introduce a new parameterised family of languages, which enables to balance expressiveness against the complexity of some queries.

A Knowledge Compilation Map for Conditional Preference Statements-based Languages

Multi-agent influence diagrams (MAIDs) are a popular form of graphical model that, for certain classes of games, have been shown to offer key complexity and explainability advantages over traditional extensive form game (EFG) representations. In this paper, we extend previous work on MAIDs by introducing the concept of a MAID subgame, as well as subgame perfect and trembling hand perfect equilibrium refinements. We then prove several equivalence results between MAIDs and EFGs. Finally, we describe an open source implementation for reasoning about MAIDs and computing their equilibria.

Equilibrium Refinements for Multi-Agent Influence Diagrams: Theory and Practice

In a multi-agent system where agents provide quantifiable work for each other on a voluntary basis, reputation mechanisms are incorporated to induce cooperation. Hereby agents assign their peers numerical scores based on their reported transaction histories. In such systems, adversaries can launch an attack by creating fake identities called Sybils, who report counterfeit transactions among one another, with the aim of increasing their own scores in the eyes of others. This  paper provides new results about the Sybil-proofness of reputation mechanisms. We revisit the impossibility result of Seuken and Parkes (2011), who show that strongly-beneficial Sybil attacks cannot be prevented on reputation mechanisms satisfying three particular requirements. We prove that, under a more rigorous set of definitions of Sybil attack benefit, this result no longer holds. We characterise  properties under which reputation mechanisms are susceptible to strongly-beneficial Sybil attacks. Building on our results, we propose a minimal set of requirements for reputation mechanisms to achieve resistance to such attacks, which are stronger   than the results by Cheng and Friedman (2005), who show Sybil-proofness of certain asymmetric reputation mechanisms.

Achieving Sybil-Proofness in Distributed Work Systems

A key issue in artificial intelligence methods for interactive preference elicitation is choosing at each stage an appropriate query to the user, in order to find a near-optimal solution as quickly as possible. A theoretically attractive method is to choose a query that minimises max setwise regret (which corresponds to the worst case loss response in terms of value of information). We focus here on the situation in which the choices are represented explicitly in a database, and with a model of user utility as a weighted sum of the criteria; in this case when the user makes a choice, an agent learns a linear constraint on the unknown vector of weights. We develop an algorithmic method for computing minimax setwise regret for this form of preference model, by making use of a SAT solver with cardinality constraints to prune the search space, and computing max setwise regret using an extreme points method. Our experimental results demonstrate the feasibility of the approach and the very substantial speed up over the state of the art.

Efficient Exact Computation of Setwise Minimax Regret for Interactive Preference Elicitation

In this paper, we propose solid semantics  to obtain the most acceptable sets of arguments. Besides, we study the application of solid semantics in the field of judgement aggregation.

Eliciting Fairness in Multiplayer Bargaining through Network-Based Role Assignment

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Promoting Fair Proposers, Fair Responders or Both? Cost-Efficient Interference in the Spatial Ultimatum Game

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