Artificial intelligence: a modern approach
Artificial intelligence: a modern approach
Competitive Markov decision processes
Competitive Markov decision processes
On the emergence of social conventions: modeling, analysis, and simulations
Artificial Intelligence - Special issue on economic principles of multi-agent systems
Online learning about other agents in a dynamic multiagent system
AGENTS '98 Proceedings of the second international conference on Autonomous agents
The dynamics of reinforcement learning in cooperative multiagent systems
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
The WALRAS Algorithm: A Convergent Distributed Implementation of General Equilibrium Outcomes
Computational Economics
Multiagent Reinforcement Learning: Theoretical Framework and an Algorithm
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Introduction to Global Optimization (Nonconvex Optimization and Its Applications)
Introduction to Global Optimization (Nonconvex Optimization and Its Applications)
Reinforcement learning: a survey
Journal of Artificial Intelligence Research
Learning conventions in multiagent stochastic domains using likelihood estimates
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
An adaptive agent bidding strategy based on stochastic modeling
Proceedings of the third annual conference on Autonomous Agents
Combinatorial auctions for supply chain formation
Proceedings of the 2nd ACM conference on Electronic commerce
Emergent Properties of a Market-based Digital Library with Strategic Agents
Autonomous Agents and Multi-Agent Systems
Agents in E-commerce: state of the art
Knowledge and Information Systems
On market-inspired approaches to propositional satisfiability
Artificial Intelligence
Nash q-learning for general-sum stochastic games
The Journal of Machine Learning Research
Cooperative Multi-Agent Learning: The State of the Art
Autonomous Agents and Multi-Agent Systems
Autonomous Adaptive Agents for Single Seller Sealed Bid Auctions
Autonomous Agents and Multi-Agent Systems
The theory and experiments of designing cooperative intelligent systems
Decision Support Systems
Use of Markov chains to design an agent bidding strategy for continuous double auctions
Journal of Artificial Intelligence Research
Conjectural equilibrium in multiuser power control games
IEEE Transactions on Signal Processing
Conjecture-based channel selection game for delay-sensitive users in multi-channel wireless networks
GameNets'09 Proceedings of the First ICST international conference on Game Theory for Networks
Conjectural equilibrium in water-filling games
GLOBECOM'09 Proceedings of the 28th IEEE conference on Global telecommunications
Dynamic conjectures in random access networks using bio-inspired learning
IEEE Journal on Selected Areas in Communications
Stackelberg contention games in multiuser networks
EURASIP Journal on Advances in Signal Processing - Special issue on game theory in signal processing and communications
Theoretical considerations of potential-based reward shaping for multi-agent systems
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
Computing a self-confirming equilibrium in two-player extensive-form games
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 3
Game theoretical applications for multi-agent systems
Expert Systems with Applications: An International Journal
An overview of cooperative and competitive multiagent learning
LAMAS'05 Proceedings of the First international conference on Learning and Adaption in Multi-Agent Systems
Learning about other agents in a dynamic multiagent system
Cognitive Systems Research
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Learning in a multiagent environment is complicated by the fact thatas other agents learn, the environment effectively changes. Moreover,other agents‘ actions are often not directly observable, and theactions taken by the learning agent can strongly bias which range ofbehaviors are encountered. We define the concept of a conjectural equilibrium, where all agents‘ expectations are realized, and each agent responds optimally to its expectations. We present a generic multiagent exchange situation, in which competitive behavior constitutes a conjectural equilibrium. We then introduce an agent that executes a more sophisticated strategic learning strategy, building a model of the response of other agents. We find that the system reliably converges to a conjectural equilibrium, but that the final result achieved is highly sensitive to initial belief. In essence, the strategic learner‘s actions tend to fulfill its expectations. Depending on the starting point, the agent may bebetter or worse off than had it not attempted to learn a model of theother agents at all.