Minimizing conflicts: a heuristic repair method for constraint satisfaction and scheduling problems
Artificial Intelligence - Special volume on constraint-based reasoning
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Communications of the ACM
An introduction to genetic algorithms
An introduction to genetic algorithms
Dynamic pricing strategies under a finite time horizon
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Decision procedures for multiple auctions
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Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Convergence Properties of the Nelder--Mead Simplex Method in Low Dimensions
SIAM Journal on Optimization
Dynamic Pricing on the Internet: Theory and Simulations
Electronic Commerce Research
Finite-time Analysis of the Multiarmed Bandit Problem
Machine Learning
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ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Structural leverage and fictitious play in sequential auctions
Eighteenth national conference on Artificial intelligence
SIMPLE - A Multi-Agent System for Simultaneous and Related Auctions
IAT '03 Proceedings of the IEEE/WIC International Conference on Intelligent Agent Technology
Probabilistic Automated Bidding in Multiple Auctions
Electronic Commerce Research
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Decision Support Systems
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Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
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EC '06 Proceedings of the 7th ACM conference on Electronic commerce
Learning algorithms for online principal-agent problems (and selling goods online)
ICML '06 Proceedings of the 23rd international conference on Machine learning
Efficient agents for cliff-edge environments with a large set of decision options
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
Efficient Bidding Strategies for Simultaneous Cliff-Edge Environments
IAT '06 Proceedings of the IEEE/WIC/ACM international conference on Intelligent Agent Technology
Bidding optimally in concurrent second-price auctions of perfectly substitutable goods
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
Proceedings of the 2006 conference on ECAI 2006: 17th European Conference on Artificial Intelligence August 29 -- September 1, 2006, Riva del Garda, Italy
Heuristic Bidding Strategies for Multiple Heterogeneous Auctions
Proceedings of the 2006 conference on ECAI 2006: 17th European Conference on Artificial Intelligence August 29 -- September 1, 2006, Riva del Garda, Italy
Learning social preferences in games
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Gender-sensitive automated negotiators
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Sequential auctions for the allocation of resources with complementarities
IJCAI'99 Proceedings of the 16th international joint conference on Artifical intelligence - Volume 1
Modeling agents through bounded rationality theories
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
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In this paper, we propose an efficient agent for competing in Cliff-Edge (CE) and simultaneous Cliff-Edge (SCE) situations. In CE interactions, which include common interactions such as sealed-bid auctions, dynamic pricing and the ultimatum game (UG), the probability of success decreases monotonically as the reward for success increases. This trade-off exists also in SCE interactions, which include simultaneous auctions and various multi-player ultimatum games, where the agent has to decide about more than one offer or bid simultaneously. Our agent competes repeatedly in one-shot interactions, each time against different human opponents. The agent learns the general pattern of the population's behavior, and its performance is evaluated based on all of the interactions in which it participates. We propose a generic approach which may help the agent compete against unknown opponents in different environments where CE and SCE interactions exist, where the agent has a relatively large number of alternatives and where its achievements in the first several dozen interactions are important. The underlying mechanism we propose for CE interactions is a new meta-algorithm, deviated virtual learning (DVL), which extends existing methods to efficiently cope with environments comprising a large number of alternative decisions at each decision point. Another competitive approach is the Bayesian approach, which learns the opponents' statistical distribution, given prior knowledge about the type of distribution. For the SCE, we propose the simultaneous deviated virtual reinforcement learning algorithm (SDVRL), the segmentation meta-algorithm as a method for extending different basic algorithms, and a heuristic called fixed success probabilities (FSP). Experiments comparing the performance of the proposed algorithms with algorithms taken from the literature, as well as other intuitive meta-algorithms, reveal superiority of the proposed algorithms in average payoff and stability as well as in accuracy in converging to the optimal action, both in CE and SCE problems.