Computational learning theory: survey and selected bibliography
STOC '92 Proceedings of the twenty-fourth annual ACM symposium on Theory of computing
Technical Note: \cal Q-Learning
Machine Learning
LEARNABLE EVOLUTION MODEL: Evolutionary Processes Guided by Machine Learning
Machine Learning - Special issue on multistrategy learning
Parallel and Distribution Simulation Systems
Parallel and Distribution Simulation Systems
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Agent-Based Modeling vs. Equation-Based Modeling: A Case Study and Users' Guide
Proceedings of the First International Workshop on Multi-Agent Systems and Agent-Based Simulation
The AQ21 Natural Induction Program for Pattern Discovery: Initial Version and its Novel Features
ICTAI '06 Proceedings of the 18th IEEE International Conference on Tools with Artificial Intelligence
If multi-agent learning is the answer, what is the question?
Artificial Intelligence
Modeling mobile agent behavior
Computers & Mathematics with Applications
Traffic Prediction for Agent Route Planning
ICCS '08 Proceedings of the 8th international conference on Computational Science, Part III
SMIZE: a spontaneous ride-sharing system for individual urban transit
MATES'09 Proceedings of the 7th German conference on Multiagent system technologies
Combining Rule Induction and Reinforcement Learning: An Agent-based Vehicle Routing
ICMLA '10 Proceedings of the 2010 Ninth International Conference on Machine Learning and Applications
An advanced adoption model and an algorithm of evaluation agents in automated supplier ranking
Computers & Mathematics with Applications
Agent-based Pickup and Delivery Planning: The Learnable Evolution Model Approach
CISIS '11 Proceedings of the 2011 International Conference on Complex, Intelligent, and Software Intensive Systems
MABS'04 Proceedings of the 2004 international conference on Multi-Agent and Multi-Agent-Based Simulation
A Comprehensive Survey of Multiagent Reinforcement Learning
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
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Multiagent-based simulation is an approach to realize stochastic simulation where both the behavior of the modeled multiagent system and dynamic aspects of its environment are implemented with autonomous agents. Such simulation provides an ideal environment for intelligent agents to learn to perform their tasks before being deployed in a real-world environment. The presented research investigates theoretical and practical aspects of learning by autonomous agents within stochastic agent-based simulation. The theoretical work is based on the Inferential Theory of Learning, which describes learning processes from the perspective of a learner's goal as a search through knowledge space. The theory is extended for approximate and probabilistic learning to account for the situations encountered when learning in stochastic environments. Practical aspects are exemplified by two use cases in autonomous logistics: learning predictive models for environment conditions in the future, and learning in the context of evolutionary plan optimization.