Knapsack problems: algorithms and computer implementations
Knapsack problems: algorithms and computer implementations
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Autonomous Agents and Multi-Agent Systems
Combinatorial Auctions
Using tabu best-response search to find pure strategy nash equilibria in normal form games
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
An Evolutionary Dynamical Analysis of Multi-Agent Learning in Iterated Games
Autonomous Agents and Multi-Agent Systems
Dynamic Testing of Wholesale Power Market Designs: An Open-Source Agent-Based Framework
Computational Economics
Alternating-offers bargaining with one-sided uncertain deadlines: an efficient algorithm
Artificial Intelligence
A multi-agent system for building project memories to facilitate the design process
Integrated Computer-Aided Engineering
Searching for approximate equilibria in empirical games
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 2
Optimal combinatorial electricity markets
Web Intelligence and Agent Systems
Essentials of Game Theory: A Concise, Multidisciplinary Introduction
Essentials of Game Theory: A Concise, Multidisciplinary Introduction
TEMMAS: The Electricity Market Multi-Agent Simulator
IWANN '09 Proceedings of the 10th International Work-Conference on Artificial Neural Networks: Part I: Bio-Inspired Systems: Computational and Ambient Intelligence
A new autonomous agent approach for the simulation of pedestrians in urban environments
Integrated Computer-Aided Engineering
On the simulation of multiagent-based regulators for physiological processes
MABS'02 Proceedings of the 3rd international conference on Multi-agent-based simulation II
IEEE Transactions on Evolutionary Computation
Construction of ontologies from object-oriented database models
Integrated Computer-Aided Engineering
Integrated Computer-Aided Engineering
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The deregulation of the electricity markets produced significant economic benefits. Improving their efficiency is a prominent scientific challenge. We focus on wholesale electricity markets, in which generators sell electricity to a public agency by means of a central auction. The multi-agent literature studies these markets according to two main approaches, each one providing a different level of expressiveness. The first approach, based on game theory, provides a formal analysis of the markets, allowing one to find the optimal generators' strategies in simple market models (e.g., omitting the auction mechanism). The second approach, based on multi-agent simulations, assumes that generators implement simple learning algorithms. This approach allows one to tackle complex market models, but no formal result on the optimality of the solution is provided in the literature. In this paper, we provide an algorithmic game theory study that improves the state of the art related to both the two previous approaches. Concerning the first approach: we enrich the game theoretic models available in the literature by introducing the auction mechanism, we provide an algorithm to solve the auction winner determination problem and an efficient algorithm to compute generators' optimal strategies. Concerning the second approach: we formally study the dynamics of learning generators analyzing the convergence to the optimal strategies.