Jeffrey S. Rosenschein

• PhD
• Professor (Full) at Hebrew University of Jerusalem

Symbolic music segmentation is the process of dividing symbolic melodies into smaller meaningful groups, such as melodic phrases. We proposed an unsupervised method for segmenting symbolic music. The proposed model is based on an ensemble of temporal prediction error models. During training, each model predicts the next token to identify musical ph...

Group decisions are often complicated by a deadline. For example, in committee hiring decisions the deadline might be the next start of a budget, or the beginning of a semester. It may be that if no candidate is supported by a strong majority, the default is to hire no one - an option that may cost dearly. As a result, committee members might prefe...

Peer reviews, evaluations, and selections are a fundamental aspect of modern science. Funding bodies the world over employ experts to review and select the best proposals from those submitted for funding. The problem of peer selection, however, is much more general: a professional society may want to give a subset of its members awards based on the...

Committees are an important scenario for reaching consensus. Beyond standard consensus-seeking issues, committee decisions are complicated by a deadline, e.g., the next start date for a budget, or the start of a semester. In committee hiring decisions, it may be that if no candidate is supported by a strong majority, the default is to hire no one--...

A key issue in cooperative game theory is coalitional stability, usually captured by the notion of the core---the set of outcomes that are resistant to group deviations. However, some coalitional games have empty cores, and any outcome in such a game is unstable. We investigate the possibility of stabilizing a coalitional game by using subsidies. W...

Iterative voting is a social choice mechanism that assumes all voters are strategic, and allows voters to change their stated preferences as the vote progresses until an equilibrium is reached (at which point no player wishes to change their vote). Previous research established that this process converges to an equilibrium for the plurality and vet...

All-pay auctions, a common mechanism for various human and agent interactions, suffers (like many other mechanisms) from the possibility of players' failure to participate in the auction. The authors model such failures and fully characterize equilibrium for this class of games, presenting a symmetric equilibrium and showing that under some conditi...

All-pay auctions, a common mechanism for various human and agent interactions, suffers, like many other mechanisms, from the possibility of players' failure to participate in the auction. We model such failures, and fully characterize equilibrium for this class of games, we present a symmetric equilibrium and show that under some conditions the equ...

We examine the surveying problem, where we attempt to predict how a target user is likely to respond to questions by iteratively querying that user, collaboratively based on the responses of a sample set of users. We focus on an active learning approach, where the next question we select to ask the user depends on their responses to the previous qu...

In multiagent systems, social choice functions can help aggregate the distinct prefer- ences that agents have over alternatives, enabling them to settle on a single choice. Despite the basic manipulability of all reasonable voting systems, it would still be desirable to find ways to reach plausible outcomes, which are stable states, i.e., a situati...

We consider iterative voting models and position them within the general framework of acyclic games and game forms. More specifically, we classify convergence results based on the underlying assumptions on the agent scheduler (the order of players) and the action scheduler (which better-reply is played). Our main technical result is providing a com...

We consider iterative voting models and position them within the general framework of acyclic games and game forms. More specifically, we classify convergence results based on the underlying assumptions on the agent scheduler (the order of players) and the action scheduler (which better-reply is played). Our main technical result is providing a com...

Peer review, evaluation, and selection is the foundation on which modern science is built. Funding bodies the world over employ experts to study and select the best proposals of those submitted for funding. The problem of peer selection, however, is much more universal: a professional society may want give a subset of its members awards based on th...

Classical results in social choice theory on the susceptibility of voting rules to strategic manipulation make the assumption that the manipulator has complete information regarding the preferences of the other voters. In reality, however, voters only have incomplete information, which limits their ability to manipulate. We explore how these limita...

We study an important crowdsourcing setting where agents evaluate one another and, based on these evaluations, a subset of agents are selected. This setting is ubiquitous when peer review is used for distributing awards in a team, allocating funding to scientists, and selecting publications for conferences. The fundamental challenge when applying c...

It is well known that standard game-theoretic approaches to voting mechanisms lead to a multitude of Nash Equilibria (NE), many of which are counter-intuitive. We focus on truth-biased voters, a model recently proposed to avoid such issues. The model introduces an incentive for voters to be truthful when their vote is not pivotal. This is a powerfu...

Following recent studies of iterative voting and its effects on plurality vote outcomes, we provide characterisations and complexity results for three models of iterative voting under the plurality rule. Our focus is on providing a better understanding regarding the set of equilibria attainable by iterative voting processes. We start with the basic...

Following recent studies of iterative voting and its effects on plurality vote outcomes, we provide characterisations and complexity results for three models of iterative voting under the plurality rule. Our focus is on providing a better understanding regarding the set of equilibria attainable by iterative voting processes. We start with the basic...

Bitcoin is an innovative decentralized cryptocurrency whose core security relies on a "proof of work" procedure, which requires network participants to repeatedly compute hashes on inputs from a large search space. Finding one of the rare inputs that generates sin extremely low hash value is considered a successful attempt, allowing miners to appro...

We study a game with \emph{strategic} vendors who own multiple items and a single buyer with a submodular valuation function. The goal of the vendors is to maximize their revenue via pricing of the items, given that the buyer will buy the set of items that maximizes his net payoff. We show this game may not always have a pure Nash equilibrium, in c...

It is well known that no reasonable voting rule is strategyproof. Moreover, the common Plurality rule is particularly prone to strategic behavior of the voters and empirical studies show that people often vote strategically in practice. Multiple game-theoretic models have been proposed to better understand and predict such behavior and the outcomes...

We consider how selfish agents are likely to share revenues derived from maintaining connectivity between important network servers. We model a network where a failure of one node may disrupt communication between other nodes as a cooperative game called the vertex Connectivity Game (CG). In this game, each agent owns a vertex, and controls all the...

We introduce Weakest Link Games (WLGs), a cooperative game modeling domains where a team's value is determined by its weakest member. The game is represented as an edge-weighted graph with designated source and target vertices, where agents are the edges. The quality of a path between the source vertex and target vertex is the minimal edge weight a...

As autonomous agents proliferate in the real world, both in software and robotic settings, they will increasingly need to band together for cooperative activities with previously unfamiliar teammates. In such ad hoc team settings, team strategies cannot be developed a priori. Rather, an agent must be prepared to cooperate with many types of teammat...

All-pay auctions, a common mechanism for various human and agent interactions, suffers, like many other mechanisms, from the possibility of players' failure to participate in the auction. We model such failures and show how they affect the equilibrium state, revealing various properties, such as the lack of influence of the most-likely-to-participa...

We study the stability of cooperative games played over an interaction network, in a model that was introduced by Myerson (1977). We show that the cost of stability of such games (i.e., the subsidy required to stabilize the game) can be bounded in terms of natural parameters of their underlying interaction networks. Specifically, we prove that if t...

Over 25 years ago, faced with a request to provide a short description of my research, I ventured that my work focused on the use of economic theory, voting theory, and game theory to establish appropriate foundations for Multiagent Systems (though the original wording was slightly different). That has remained an accurate description of my researc...

We study the effects of bidder collaboration in all-pay auctions. We analyse both mergers, where the remaining players are aware of the agreement between the cooperating participants, and collusion, where the remaining players are unaware of this agreement. We examine two scenarios: the sum-profit model where the auctioneer obtains the sum of all s...

Voting is widely used to aggregate the different preferences of agents, even though these agents are often able to manipulate the outcome through strategic voting. Most research on manipulation of voting methods studies (1) limited solution concepts, (2) limited preferences, or (3) scenarios with a few manipulators that have a common goal. In contr...

Efficient management of resources in a society is a key ingredient of many multiagent systems. Self-interested agents (either human or automated) working to maximize their own benefit might make excessive use of a common resource, a situation known as the "tragedy of the commons". Therefore, game-theoretic considerations should come into play in th...

Bayes-Nash Equilibrium (BNE) is at the root of many significant applications of modern game theory to multiagent systems, ranging from airport security scheduling, to network analysis, to mechanism design in e-commerce. However, the computational complexity of calculating BNEs makes the process prohibitively costly, and the process does not scale w...

The strategyproof classification problem deals with a setting where a decision maker must classify a set of input points with binary labels, while minimizing the expected error. The labels of the input points are reported by self-interested agents, who might lie in order to obtain a classifier that more closely matches their own labels, thereby cre...

In multiagent systems, social choice functions can help aggregate the distinct preferences that agents have over alternatives, enabling them to settle on a single choice. Despite the basic manipulability of all reasonable voting systems, it would still be desirable to find ways to reach a stable result, i.e., a situation where no agent would wish t...

In recent papers, Obraztsova et al. initiated the study of the computational complexity of voting manipulation under randomized tie-breaking [3, 2]. The authors provided a polynomial-time algorithm for the problem of finding an optimal vote for the manipulator (a vote maximizing the manipulator's expected utility) under the Maximin voting rule, for...

Multiagent research provides an extensive literature on formal Beliefs-Desires-Intentions (BDI) based models describing the notion of teamwork and cooperation. However, multiagent environments are often not cooperative nor collaborative; in many cases, agents have conflicting interests, leading to adversarial interactions. This form of interaction...

Peer-to-peer frameworks are known to be robust and scal- able to large numbers of agents. Recent resource allocation studies have leveraged this by using peer-to-peer frameworks for the implementation of resource matching algorithms. In this paper, we present a matching protocol for multiagent resource allocation in a competitive peer-to-peer envir...

Social choice theory and cooperative (coalitional) game theory have become important foundations for the design and analysis of multiagent systems. In this paper, we use cooperative game theory tools in order to explore the coalition formation process in the coalitional manipulation problem. Unlike earlier work on a cooperative-game-theoretic appro...

Experts reporting the labels used by a learning algorithm cannot always be assumed to be truthful. We describe recent advances in the design and analysis of strategyproof mechanisms for binary classification, and their relation to other mechanism design problems.

Previous research in Artificial Intelligence has identified the possibility of simplifying planning problems via the identification and exploitation of symmetries. We advance the state of the art in algorithms that exploit symmetry in planning problems by generalizing previous approaches, and applying symmetry reductions to state-based planners. We...

Cooperation among automated agents is becoming increasingly important in various artificial intelligence applications. Coalitional (i.e., cooperative) game theory supplies conceptual and mathematical tools useful in the analysis of such interactions, and in particular in the achievement of stable outcomes among self-interested agents. Here, we stud...

Identifying the corporate controller (controlling shareholder, ultimate owner) is an essential prerequisite for any debate on the corporate governance of a specific firm and of entire markets. This paper aims to provide a comprehensive, precise and economically sound method for identifying control relations on the corporate level and especially in...

We consider an automated agent that needs to coordinate with a human partner when communication between them is not possible or is undesirable (tacit coordination games). Specifically, we examine situations where an agent and human attempt to coordinate their choices among several alternatives with equivalent utilities. We use machine learning algo...

We introduce a new algorithm for the Unweighted Coalitional Manipulation problem under the Maximin voting rule. We prove that the algorithm gives an approximation ratio of 1 2/3 to the corresponding optimization problem. This is an improvement over the previously known algorithm that gave a 2-approximation. We also prove that its approximation rati...

Strategyproof (SP) classification considers situations in which a decision-maker must classify a set of input points with binary labels, minimizing expected error. Labels of input points are reported by self-interested agents, who may lie so as to obtain a classifier more closely matching their own labels. These lies would create a bias in the data...

Previous research in Artificial Intelligence has identified the possibility of simplifying planning problems via the identification and exploitation of symmetries. We advance the state of the art in algorithms that exploit symmetry in planning problems by generalizing previous approaches, and applying symmetry reductions to state-based planners. We...

A key solution concept in cooperative game theory is the core. The core of an expense sharing game contains stable allocations of the total cost to the participating players, such that each subset of players pays at most what it would pay if acting on its own. Unfortunately, some expense sharing games have an empty core, meaning that the total cost...

Teams of agents may not always be developed in a planned, coordinated fashion. Rather, as deployed agents become more common Teams of agents may not always be developed in a planned, coordinated fashion. Rather, as deployed agents become more common in e-commerce and other settings, there are increasing opportunities for previously unacquainted age...

This article explores hybrid agents that use a variety of techniques to improve their performance in an environment over time. We considered, specifically, genetic-learning-parenting hybrid agents, which used a combination of a genetic algorithm, a learning algorithm (in our case, reinforcement learning), and a parenting algorithm, to modify their...

In various domains, such as computer games, robotics, and transportation networks, shortest paths may need to be found quickly. Search time can be significantly reduced if it is known which parts of the graph include "swamps" - areas that cannot lie on the only available shortest path, and can thus safely be pruned during search. We introduce an al...

We introduce Transformation Games (TGs), a form of coalitional game in which players are endowed with sets of initial resources, and have capabilities allowing them to derive certain output resources, given certain input resources. The aim of a TG is to generate a particular target resource; players achieve this by forming a coalition capable of pe...

As autonomous agents proliferate in the real world, both in software and robotic settings, they will increasingly need to band together for cooperative activities with previously unfamiliar teammates. In such ad hoc team settings, team strategies cannot be developed a priori. Rather, an agent must be prepared to cooperate with many types of teammat...

Multi-agent decision problems, in which independent agents have to agree on a joint plan of action or allocation of resources, are central to AI. In such situations, agents' individual preferences over available alternatives may vary, and they may try to reconcile these differences by voting. Based on the fact that agents may have incentives to vot...

In various domains, such as computer games, robotics, and transportation networks, shortest paths may need to be found quickly. Search time can be significantly reduced if it is known which parts of the graph include ``swamps''---areas that cannot lie on the only available shortest path, and can thus safely be pruned during search. We introduce an...

Multi-agent decision problems, in which independent agents have to agree on a joint plan of action or allocation of resources, are central to AI. In such situations, agents' individual preferences over available alternatives may vary, and they may try to reconcile these differences by voting. Based on the fact that agents may have incentives to vot...

Many multiagent domains where cooperation among agents is crucial to achieving a common goal can be modeled as coalitional games. However, in many of these domains, agents are unequal in their power to affect the outcome of the game. Prior research on weighted voting games has explored power indices, which reflect how much “real power” a voter has....

In various domains, such as computer games, robotics, and transportation networks, shortest paths may need to be found quickly. Search time can be significantly reduced if it is known which parts of the graph include "swamps"—areas that cannot lie on the only available shortest path, and can thus safely be pruned during search. We introduce an algo...

In the strategypro of classification setting, a set of labeled examples is partitioned among multiple agents. Given the reported labels, an optimal classification mechanism returns a classifier that minimizes the number of mislabeled examples. However, each agent is interested in the accuracy of the returned classifier on its own examples, and may...

As autonomous agents proliferate in the real world, both in software and robotic settings, they will increas- ingly need to band together for cooperative activities with previously unfamiliar teammates. In such ad hoc team settings, team strategies cannot be developed a priori. Rather, an agent must be prepared to cooper- ate with many types of tea...

We consider a simple model of cooperation among agents called Coalitional Skill Games (CSGs). This is a restricted form of coalitional games, where each agent has a set of skills that are required to complete various tasks. Each task requires a set of skills in order to be completed, and a coalition can accomplish the task only if the coalitionʼs a...

Decentralized Reputation Systems have recently emerged as a prominent method of establishing trust among self-interested agents in online environments. A key issue is the efficient aggregation of data in the system; several approaches have been proposed, but they are plagued by major shortcomings. We put forward a novel, decentralized data manageme...

Heuristic functions play a crucial rule in optimal planning , and the theoretical limitations of algorithms using such fu nc- tions are therefore of interest. Much work has focused on finding bounds on the behavior of heuristic search algorithm s, using heuristics with specific attributes. Recently, it has been shown that in domains that contain tr...

The core of a cooperative game contains all stable distributions of a coalition’s gains among its members. However, some games have an empty core, with every distribution being unstable. We allow an external party to offer a supplemental payment to the grand coalition, which may stabilize the game, if the payment is sufficiently high. We consider t...

We consider Effort Games , a game‐theoretic model of cooperation in open environments, which is a variant of the principal‐agent problem from economic theory. In our multiagent domain, a common project depends on various tasks; carrying out certain subsets of the tasks completes the project successfully, while carrying out other subsets does not. T...

Scoring rules and voting trees are two broad and concisely-representable classes of voting rules; scoring rules award points to alternatives according to their position in the preferences of the voters, while voting trees are iterative procedures that select an alternative based on pairwise comparisons. In this paper, we investigate the PAC-learnab...

A key question in cooperative game theory is that of coalitional stability, usually captured by the notion of the core—the set of outcomes such that no subgroup of players has an incentive to deviate. However, some coalitional games have empty cores, and any outcome in such a game is unstable. In this paper, we investigate the possibility of stabil...

Modern peer-to-peer file sharing systems rely heavily on the willingness of users to distribute files to others. A selfish user can choose to download a file and consume resources without uploading in return. This form of free-riding plagues all currently deployed peer-to-peer systems. We present a novel protocol for a BitTorrent-like system (i.e.,...

We investigate the problem of coalitional manipulation in elections, which is known to be hard in a variety of voting rules. We put forward efficient algorithms for the problem in Borda, Maximin and Plurality with Runoff, and analyze their windows of error. Specifically, given an instance on which an algorithm fails, we bound the additional power t...

Preference aggregation is used in a variety of multiagent applications, and as a result, voting theory has become an important topic in multiagent system research. However, power indices (which reflect how much “real power” a voter has in a weighted voting system) have received relatively little attention, although they have long been studied in po...

The voting rules proposed by Dodgson and Young are both designed to find the alternative closest to being a Condorcet winner, according to two different notions of proximity; the score of a given alternative is known to be hard to compute under either rule. In this paper, we put forward two algorithms for approximating the Dodgson score: an LP- bas...

Recommender systems attempt to highlight items that a target user is likely to find interesting. A common technique is to use collaborative filtering (CF), where multiple users share information so as to provide each with effective recommendations. A key aspect of CF systems is finding users whose tastes accurately reflect the tastes of some target...

Understanding the computational complexity of manipulation in elections is arguably the most central agenda in Computational Social Choice. One of the influential variations of the the problem involves a coalition of manipulators trying to make a favorite candidate win the election. Although the complexity of the problem is well-studied under the a...

Strategyproof classification deals with a setting where a decision-maker must classify a set of in- put points with binary labels, while minimizing the expected error. The labels of the input points are reported by self-interested agents, who might lie in order to obtain a classifier that more closely matches their own labels, thus creating a bias...

One key question in cooperative game theory is that of coalitional stability. A coalition in such games is stable when no subset of the agents in it has a rational incentive to leave the coalition. Finding a division of the gains of the coalition (an imputation) lies at the heart of many cooperative game theory solution concepts, the most prominent...

In a variety of domains, such as computer games and robotics, many shortest paths have to be found quickly in real time. We address the problem of quickly finding shortest paths in large known graphs. We propose a method that relies on identifying areas that tend to be searched needlessly (areas we call swamp-regions), and exploits this knowledge t...

A key question in cooperative game theory is that of coalitional sta- bility, usually captured by the notion of the core—the set of outcomes such that no subgroup of players has an incentive to deviate. However, some coalitional games have empty cores, and any outcome in such a game is unstable. In this paper, we investigate the possibility of stab...

We study the computational aspects of information elicitation mechanisms in which a principal attempts to elicit the private information of other agents using a carefully selected payment scheme based on proper scoring rules. Scoring rules, like many other mechanisms set in a probabilistic environment, assume that all participating agents share som...

Although recent years have seen a surge of interest in the computational aspects of social choice, no specific attention has previously been devoted to elections with multiple winners, e.g., elections of an assembly or committee. In this paper, we characterize the worst-case complexity of manipulation and control in the context of four prominent mu...

We introduce a multi-model variant of the EMT-based control algorithm. The new algorithm, MM-EMT, is capable of balancing several control tasks expressed using separate dynamic models with a common action space. Such multiple models are common in both single-agent environments, when the agent has multiple tasks to achieve, and in team activities, w...

We present an extension of the Dynamics Based Control (DBC) paradigm to environment models based on Predictive State Representations (PSRs). We show an approximate greedy version of the DBC for PSR model, EMT-PSR, and demonstrate how this algorithm can be applied to solve several control problems. We then provide some classifications and requiremen...

We consider Effort Games, a game theoretic model of cooperation in open environments, which is a variant of the principal--agent problem from economic theory. In our multiagent domain, a common project depends on various tasks; achieving certain subsets of the tasks completes the project successfully, while others do not. The probability of achievi...

We consider Effort Games, a game theoretic model of cooperation in open environments, which is a variant of the principal-agent problem from economic theory. In our multiagent domain, a common project depends on various tasks; achieving certain subsets of the tasks completes the project successfully, while others do not. The probability of achievin...

We demonstrate that winner selection in two prominent proportional representation voting systems is a computationally intractable problem—implying that these systems are impractical when the assembly is large. On a different note, in settings where the size of the assembly is constant, we show that the problem can be solved in polynomial time.

We investigate the problem of coalitional manipulation in elections, which is known to be hard in a variety of voting rules. We put forward efficient algorithms for the problem in Borda, Maximin and Plurality with Runoff, and analyze their windows of error. Specifically, given an instance on which an algorithm fails, we bound the additional power t...

Multiagent research provides an extensive literature on formal Belief-Desire-Intention (BDI) based models describing the notions of teamwork and cooperation, but adversarial and competitive rela- tionships have received very little formal BDI treatment. Moreover, one of the main roles of such models is to serve as design guide- lines for the creati...

Scoring rules are a broad and concisely-representable class of voting rules which includes, for example, Plurality and Borda. Our main result asserts that the class of scoring rules, as functions from preferences into candidates, is e- ciently learnable in the PAC model. We discuss the appli- cations of this result to automated design of scoring ru...

We consider computational aspects of a game theoretic ap- proach to network reliability. Consider a network where fail- ure of one node may disrupt communication between two other nodes. We model this network as a simple coalitional game, called the vertex Connectivity Game (CG). In this game, each agent owns a vertex, and controls all the edges go...

Many multiagent domains where cooperation among agents is crucial to achieving a common goal can be modeled as coalitional games. However, in many of these domains, agents are unequal in their power to affect the outcome of the game. Prior research on weighted voting games has explored power indices, which reflect how much "real power" a voter has....

We consider a multiagent resource allocation domain where the marginal production of each resource is diminishing. A set of identical, self-interested agents requires access to sharable resources in the domain. We present a distributed and random allocation procedure, and demonstrate that the allocation converges to the optimal in terms of utilitar...

We consider Coalitional Skill Games (CSGs), a simple model of cooperation among agents. This is a restricted form of coalitional games, where each agent has a set of skills that are required to complete various tasks. Each task requires a set of skills in order to be completed, and a coalition can accomplish the task only if the coalition's agents...

Recent work by Procaccia, Rosenschein and Zohar [14] established some results regarding the complexity of manipulation and control in elections with multiple winners, such as elections of an assembly or committee; that work provided an initial understanding of the topic. In this paper, we paint a more complete picture of the topic, investigating fo...

We consider a multiagent extension of single-agent graph col- oring. Multiple agents hold disjoint autonomous subgraphs of a global graph, and every color used by the agents in coloring the graph has associated cost. In this multiagent graph color- ing scenario, we seek a minimum legal coloring of the global graph's vertices, such that the coloring...

We consider the following setting: a decision maker must make a decision based on reported data points with binary labels. Subsets of data points are controlled by different self- ish agents, which might misreport the labels in order to sway the decision in their favor. We design mechanisms (both de- terministic and randomized) that reach an approx...

When autonomous agents attempt to coordinate action, it is often necessary that they reach some kind of consensus. Reaching such a consensus has traditionally been dealt with in the Distributed Artific.ial Intelligence literature via the mechanism of negotiation. Another alternative is to have agents bypass negotiation by using a voting mech- anism...

The voting rules proposed by Dodgson and Young are both designed to find the candidate closest to being a Condorcet winner, according to two different notions of proximity; the score of a given candidate is known to be hard to compute under both rules. In this paper, we put forward an LP-based randomized rounding algorithm which yields an O(log m)...