Parameterized algorithmics for computational social choice: Nine research challenges

Computational social choice is a new interdisciplinary research area at the intersection of social choice theory and computer science that studies computational aspects of social choice mechanisms as well as applications of social choice ideas in the design and analysis of computing systems. Research on computational social choice now encompasses a variety of topics, including the algorithmic aspects of voting rules, computational barriers to strategic behaviour, collective decision-making in multi-agent systems, preference elicitation and communication issues in voting, fair division, computational aspects of weighted voting games, collective decision-making in combinatorial domains, logic-based formalisms for social choice problems, belief and judgement aggregation, and social software. The aim of this special issue is to build on the spectacular success of two workshops on computational social choice (COMSOC’06, Amsterdam and COMSOC’08, Liverpool), and provide a forum for reseachers in the area to present their ideas on a variety of topics. This special issue is composed of nine research articles and a short note that span a fairly wide range of issues, including voting, mechanism design, judgment aggregation, and fairness, and a similarly wide range of techniques, such as logic, experiments, and computational complexity. The paper by Agotnes, van der Hoek and Wooldridge defines a new modal logic for representing and reasoning about preference and judgment aggregation. The authors show that their logic can be used to express a variety of concepts and results from the social choice domain, including majority voting, the discursive paradox, Arrow’s theorem and Condorcet’s paradox. Baharad, Goldberger, Koppel and Nitzan propose a maximum likelihood estimation-based approach for aggregating voters’ opinions over several issues. They argue that their method is vastly superior to simple majority voting, and use both analytical and empirical evaluation to support their argument. Balan, Richards and Luke deal with the problem of selecting a sequence of actions when the actions have multiple beneficiaries. Their goal is to select the actions according to a lexicographic fairness criterion. The explore the complexity of this problem for both finite and infinite sequences of actions and present both algorithms and intractability results for this problem. The paper by Caminada and Pigozzi applies argumentation theory to judgment aggregation, and proposes aggregation operators satisfying not only the usual property of collective rationality, but another highly desirable property,

Computational social choice is an interdisciplinary field of study at the interface of social choice theory and computer science, with knowledge flowing in either direction. On the one hand, computational social choice is concerned with importing concepts and procedures from social choice theory for solving questions that arise in computer science and AI application domains. This is typically the case for managing societies of autonomous agents, which calls for negotiation and voting procedures. On the other hand, computational social choice is concerned with importing notions and methods from computer science for solving questions originally stemming from social choice, for instance by providing new perspectives on the problem of manipulation and control in elections. This Dagstuhl Seminar has been devoted to the presentation of recent results and an exchange of ideas in this growing research field.

Judgment aggregation is a mathematical theory of collective decision-making. It concerns the methods whereby individual opinions about logically interconnected issues of interest can, or cannot, be aggregated into one collective stance. Aggregation problems have traditionally been of interest for disciplines like economics and the political sciences, as well as philosophy, where judgment aggregation itself originates from, but have recently captured the attention of disciplines like computer science, artificial intelligence and multi-agent systems. Judgment aggregation has emerged in the last decade as a unifying paradigm for the formalization and understanding of aggregation problems. Still, no comprehensive presentation of the theory is available to date. This Synthesis Lecture aims at filling this gap presenting the key motivations, results, abstractions and techniques underpinning it.

This chapter explores relationships between the science and profession of psychology and the development of artificial intelligence (AI) and robotic systems. It briefly examines the role of psychology in the development of AI systems and theories. There is a long and intimate history of such a relationship, exemplified in the arts and the social sciences as well as in computer science and engineering. The chapter examines the role that psychology has played in the understanding of the ways in which humans interact with contemporary AI systems, to influence the behaviour of the systems and, in turn, to be influenced by them. Human decision making has been shown to be improved by factors other than intelligence and thereby appears to be a quality which would be retained by humans rather than machines, for example motivational processes, emotions and experience. The chapter examines the state of the profession of psychology and helping professions in the light of developments in AI.

In recent years, there has been rising interest in a field called behavioral finance, which incorporates psychological methods in analysing investor behavior. The aim of this chapter is to study the technical and the fundamental investing strategy of financial market participants dealing with assets. The motivation of the presented research is to simulate the financial market in the form of agent-based model and to investigate various impacts of risk and transaction costs on its stability. Computational social science involves the use of agent based modeling and simulation to study complex social systems. It is related to a variety of other simulation techniques, including discrete event simulation and distributed artificial intelligence or Multi-Agent Systems (MAS). In practice, each agent has only partial knowledge of other agents and each agent makes its own decisions based on the partial knowledge about other agents in the system. For purposes of this chapter, a MAS will be implemented as a simulation framework in JADE development platform. The hypothesis was that transaction costs introduction will stabilize the financial market. The results obtained show that in the case of risk involvement into the system the hypothesis can be fulfilled only partially.

The field of “intelligent agents and multi-agent systems” is maturing; no longer is it a special topic to be introduced to graduate students after years of training in computer science and many introductory courses in artificial intelligence. Instead, the time is ripe to introduce agents and multi-agents directly to undergraduate students, whether majoring in computer science or not. This chapter focuses on exactly this challenge, drawing on the co-authors’ experience of teaching several such undergraduate courses on agents and multi-agents, over the last three years at two different universities. The chapter outlines three key issues that must be addressed. The first issue is facilitating students’ intuitive understanding of fundamental concepts of multi-agent systems; the authors illustrate uses of science fiction materials and classroom games to not only provide students with the necessary intuitive understanding but with the excitement and motivation for studying multi-agent systems. The second is in selecting the right material — either science-fiction material or games — for providing students the necessary motivation and intuition; we outline several criteria that have been useful in selecting such material. The third issue is in educating students about the fundamental philosophical, ethical and social issues surrounding agents and multi-agent systems:they outline course materials and classroom activities that allow students to obtain this “big picture” futuristic vision of our science. The authors conclude with feedback received, lessons learned and impact on both the computer science students and non computer-science students.

The primary challenge in the development of large-scale artificial intelligence (AI) systems lies in achieving scalable decision-making—extending the AI models while maintaining sufficient performance. Existing research indicates that distributed AI can improve scalability by decomposing complex tasks and distributing them across collaborative nodes. However, previous technologies suffered from compromised real-world applicability and scalability due to the massive requirement of communication and sampled data. Here we develop a model-based decentralized policy optimization framework, which can be efficiently deployed in multi-agent systems. By leveraging local observation through the agent-level topological decoupling of global dynamics, we prove that this decentralized mechanism achieves accurate estimations of global information. Importantly, we further introduce model learning to reinforce the optimal policy for monotonic improvement with a limited amount of sampled data. Empirical results on diverse scenarios show the superior scalability of our approach, particularly in real-world systems with hundreds of agents, thereby paving the way for scaling up AI systems.

Multi-agent learning is a crucial method to control or find solutions for systems, in which more than one entity needs to be adaptive. In today's interconnected world, such systems are ubiquitous in many domains, including auctions in economics, swarm robotics in computer science, and politics in social sciences. Multi-agent learning is inherently more complex than single-agent learning and has a relatively thin theoretical framework supporting it. Recently, multi-agent learning dynamics have been linked to evolutionary game theory, allowing the interpretation of learning as an evolution of competing policies in the mind of the learning agents. The dynamical system from evolutionary game theory that has been linked to Q-learning predicts the expected behavior of the learning agents. Closer analysis however allows for two interesting observations: the predicted behavior is not always the same as the actual behavior, and in case of deviation, the predicted behavior is more desirable. This discrepancy is elucidated in this article, and based on these new insights Frequency Adjusted Q- (FAQ-) learning is proposed. This variation of Q-learning perfectly adheres to the predictions of the evolutionary model for an arbitrarily large part of the policy space. In addition to the theoretical discussion, experiments in the three classes of two-agent two-action games illustrate the superiority of FAQ-learning.

Research in both computational social choice and preference reasoning uses tools and techniques from computer science, generally algorithms and complexity analysis, to examine topics in group decision making. This has brought tremendous progress in the last decades, creating new avenues for research and results in areas including voting and resource allocation. I argue that of equal importance to the theoretical results are impacts in research and development from the empirical part of the computer scientists toolkit: data, system building, and human interaction. I highlight work by myself and others to establish data driven, application driven research in the computational social choice and preference reasoning areas. Along the way, I highlight interesting application domains and important results from the community in driving this area to make concrete, real-world impact.

A New Approach In Learning For Intelligent Multi Agent Systems

Artificial intelligence and radiologists in prostate cancer detection on MRI (PI-CAI): an international, paired, non-inferiority, confirmatory study

The agent concept is one of the very few concepts which have been able to cross the borders of scientific disciplines, finding applications not only in computer science and artificial intelligence, but also in biology, economics, engineering, and the social sciences. Thus it is only natural that the research frontiers in this fast growing field develop along interdisciplinary problems. These include the ability of agents to evolve, e.g. through adaptation and learning, but also the emergence of systemic properties which result from the interplay of large numbers of agents, rather than from carefully designed single agent behavior. This topical issue combines original scientific papers selected from two satellite workshops that were held in conjunction with AAMAS 2010, the Ninth International Conference on Autonomous Agents and Multi-Agent Systems. Each of these workshops had its own focus. WEIN’2010 — The Fifth International Workshop on Emergent Intelligence on Networked Agents has put forward the complex networks approach as a framework to model multi-agent systems, i.e. agents are represented by nodes and their interactions are represented by links. Thus, the underlying network structure of a multiagent system plays a crucial role in explaining emergent properties. Networked agents, on the other hand, may be able to actively change this structure by forming new links or cutting existing ones. The selected contributions from this workshop

Extract Large and complex datasets collecting digital breadcrumbs of human behaviour have become non-negligible sources for social science researchers, students, and practitioners. ‘Big data’ has made a place for itself as a commodity in academic, commercial, and non-commercial settings. There have been numerous new descriptions for defining social scientists with quantitative expertise: empirical social scientist, computational social scientist, quantitative social scientists, social data scientist, etc. Nonetheless, a social scientist with solid big data skills is not yet a common profile in academia and research institutions. Neither is a computer scientist with broad interdisciplinary skills in social sciences. As the amount of published material increases exponentially in many disciplines, specialisation seems to be a reasonable response to keep up with the state of the art, and to produce technically and conceptually solid research. Interdisciplinary teams are vital in providing the critical synthesis from these separate specialisations, and this book is put together to help bridge the language gap between the different specialisations needed for migration analysis from big data sources.

In cases where the success of artificial intelligence (AI) approaches is more focused on the multi-agent system (MAS) than those that may make AI understandable to humans, in light of the repetitive cycles of the history of AI, we expect to see technology reevaluation that is commonly referred to as 'classical IA' – especially agent and MAS – in the upcoming years. Other than that, agents and multi-agent (MAS) systems are crucial to designing intelligent systems from the start. They can open new possibilities to researchers for explanatory and intelligent systems through their long-term link with logical technology, which is characteristic of its early times. That is why it is now crucial to comprehend the present position of MAS for medical image methods. This paper thus seeks to provide a complete overview by reviewing the MAS and the agent approaches. Two different views are taken and assessed for the resulting technologies: the MAS and the image processing technologies.

BackgroundThe surgical difficulty of laparoscopic cholecystectomy (LC) for acute cholecystitis (AC) and the risk of bile duct injury (BDI) depend on the degree of fibrosis and scarring caused by inflammation; therefore, understanding these intraoperative findings is crucial to preventing BDI. Scarring makes it particularly difficult to perform safely and increases the BDI risk. This study aimed to develop an artificial intelligence (AI) system to indicate intraoperative findings of scarring in LC for AC.Materials and methodsAn AI system was developed to detect scarred areas using an algorithm for semantic segmentation based on deep learning. The training dataset consisted of 2025 images extracted from LC videos of 21 cases with AC. External evaluation committees (EEC) evaluated the AI system on 20 cases of untrained data from other centers. EECs evaluated the accuracy in identifying the scarred area and the usefulness of the AI system, which were assessed based on annotation and a 5-point Likert-scale questionnaire.ResultsThe average DICE coefficient for scarred areas between AI detection and EEC annotation was 0.612. The EEC’s average detection accuracy on the Likert scale was 3.98 ± 0.76. AI systems were rated as relatively useful for both clinical and educational applications.ConclusionWe developed an AI system to detect scarred areas in LC for AC. Since scarring increases the surgical difficulty, this AI system has the potential to reduce BDI.