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Strategic Voting
Social choice theory deals with aggregating the preferences of multiple individuals regarding several available alternatives, a situation colloquially known as voting. There are many different voting rules in use and even more in the literature, owing to the various considerations such an aggregation method should take into account. The analysis of voting scenarios becomes particularly challenging in the presence of strategic voters, that is, voters that misreport their true preferences in an attempt to obtain a more favorable outcome. In a world that is tightly connected by the Internet, where multiple groups with complex incentives make frequent joint decisions, the interest in strategic v...
Algorithmic Game Theory
Thepresentvolumewasdevotedto thethirdeditionofthe InternationalSym- sium on Algorithmic Game Theory (SAGT), an interdisciplinary scienti?c event intended to provide a forum for researchers as well as practitioners to exchange innovative ideas and to be aware of each other's e?orts and results. SAGT 2010 took place in Athens, on October 18–20, 2010. The present volume contains all contributed papers presented at SAGT 2010 together with the distinguished invited lectures of Amos Fiat (Tel-Aviv University, Israel), and Paul Goldberg (University of Liverpool, UK). The two invited papers are presented at the - ginning of the proceedings, while the regular papers follow in alphabetical order (by...
Mechanisms for Stability and Welfare: Increasing Cooperation among Self-interested Agents
Too often an interaction among self-interested parties leads to an outcome that is not in the best interest of any of them. In this thesis, I look at such interactions as games, so that the loss of stability and welfare can be measured and studied using the standard concepts of game theory such as equilibrium and utility. I study and design mechanisms that alter these games in order to induce more cooperation, stable outcomes, and higher utility for the participants.
Algorithmic Game Theory
This book constitutes the refereed proceedings of the 9th International Symposium on Algorithmic Game Theory, SAGT 2016, held in Liverpool, UK, in September 2016.The 26 full papers presented together with 2 one-page abstracts were carefully reviewed and selected from 62 submissions. The accepted submissions cover various important aspectsof algorithmic game theory such as computational aspects of games, congestion games and networks, matching and voting, auctions and markets, and mechanism design. /div
Algorithmic Game Theory
This book constitutes the refereed proceedings of the 12th International Symposium on Algorithmic Game Theory, SAGT 2019, held in Athens, Greece, in September/October 2019. The 25 full papers presented together with 3 invited talks and one abstract paper were carefully reviewed and selected from 55 submissions. The papers are organized in topical sections named: Algorithmic Mechanism Design; Auctions and Markets; Computational Aspects of Games; Network Games and Congestion Games; Social Choice; and Matchings and Fair Division.
Algorithmic Game Theory
This book constitutes the proceedings of the 15th International Symposium on Algorithmic Game Theory, SAGT 2022, which took place in Colchester, UK, in September 2022. The 31 full papers included in this book were carefully reviewed and selected from 83 submissions. They were organized in topical sections as follows: Auctions, markets and mechanism design; computational aspects in games; congestion and network creation games; data sharing and learning; social choice and stable matchings.
An Introduction to the Planning Domain Definition Language
Planning is the branch of Artificial Intelligence (AI) that seeks to automate reasoning about plans, most importantly the reasoning that goes into formulating a plan to achieve a given goal in a given situation. AI planning is model-based: a planning system takes as input a description (or model) of the initial situation, the actions available to change it, and the goal condition to output a plan composed of those actions that will accomplish the goal when executed from the initial situation. The Planning Domain Definition Language (PDDL) is a formal knowledge representation language designed to express planning models. Developed by the planning research community as a means of facilitating ...
Algorithmic Game Theory
This book constitutes the refereed proceedings of the Second International Symposium on Algorithmic Game Theory, SAGT 2009, held in Paphos, Cyprus, in October 2009. The 29 revised full papes presented together with 3 invited lectures were carefully reviewed and selected from 55 submissions. The papers are intended to cover all important areas such as solution concepts, game classes, computation of equilibria and market equilibria, algorithmic mechanism design, automated mechanism design, convergence and learning in games, complexity classes in game theory, algorithmic aspects of fixed-point theorems, mechanisms, incentives and coalitions, cost-sharing algorithms, computational problems in economics, finance, decision theory and pricing, computational social choice, auction algorithms, price of anarchy and its relatives, representations of games and their complexity, economic aspects of distributed computing and the internet, congestion, routing and network design and formation games and game-theoretic approaches to networking problems.
Algorithmic Decision Theory
This book constitutes the thoroughly refereed conference proceedings of the 4th International Conference on Algorithmic Decision Theory , ADT 2015, held in September 2015 in Lexington, USA. The 32 full papers presented were carefully selected from 76 submissions. The papers are organized in topical sections such as preferences; manipulation, learning and other issues; utility and decision theory; argumentation; bribery and control; social choice; allocation and other problems; doctoral consortium.
Applying Reinforcement Learning on Real-World Data with Practical Examples in Python
Reinforcement learning is a powerful tool in artificial intelligence in which virtual or physical agents learn to optimize their decision making to achieve long-term goals. In some cases, this machine learning approach can save programmers time, outperform existing controllers, reach super-human performance, and continually adapt to changing conditions. This book argues that these successes show reinforcement learning can be adopted successfully in many different situations, including robot control, stock trading, supply chain optimization, and plant control. However, reinforcement learning has traditionally been limited to applications in virtual environments or simulations in which the set...
