Intelligence without representation
Artificial Intelligence
Automatic programming of behavior-based robots using reinforcement learning
Artificial Intelligence
Issues in evolutionary robotics
Proceedings of the second international conference on From animals to animats 2 : simulation of adaptive behavior: simulation of adaptive behavior
Proceedings of the workshop on Computational learning theory and natural learning systems (vol. 2) : intersections between theory and experiment: intersections between theory and experiment
Robot shaping: developing autonomous agents through learning
Artificial Intelligence
Incremental evolution of complex general behavior
Adaptive Behavior - Special issue on environment structure and behavior
Learning from History for Behavior-Based Mobile Robots in Non-Stationary Conditions
Machine Learning - Special issue on learning in autonomous robots
Reinforcement learning with hierarchies of machines
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Layered control architectures in robots and vertebrates
Adaptive Behavior
Between MDPs and semi-MDPs: a framework for temporal abstraction in reinforcement learning
Artificial Intelligence
Robot Shaping: An Experiment in Behavior Engineering
Robot Shaping: An Experiment in Behavior Engineering
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Evolutionary Robotics: The Biology,Intelligence,and Technology
Evolutionary Robotics: The Biology,Intelligence,and Technology
Neuro-Dynamic Programming
Reinforcement Learning in the Multi-Robot Domain
Autonomous Robots
Autonomous Robots
Recent Advances in Hierarchical Reinforcement Learning
Discrete Event Dynamic Systems
Policy Invariance Under Reward Transformations: Theory and Application to Reward Shaping
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Algorithms for Inverse Reinforcement Learning
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Advances in Neural Information Processing Systems 5, [NIPS Conference]
Selected Papers from AISB Workshop on Evolutionary Computing
A Study on the use of "self-generation'' in memetic algorithms
Natural Computing: an international journal
An analysis of cooperative coevolutionary algorithms
An analysis of cooperative coevolutionary algorithms
A New Memetic Algorithm for the Asymmetric Traveling Salesman Problem
Journal of Heuristics
Understanding cooperative co-evolutionary dynamics via simple fitness landscapes
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Cooperative Coevolution: An Architecture for Evolving Coadapted Subcomponents
Evolutionary Computation
Adaptive Behavior in Autonomous Agents
Presence: Teleoperators and Virtual Environments
Evolutionary Function Approximation for Reinforcement Learning
The Journal of Machine Learning Research
Analyzing cooperative coevolution with evolutionary game theory
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Evolution of a subsumption architecture neurocontroller
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology - AILS '04
New methods for competitive coevolution
Evolutionary Computation
A robot that walks; emergent behaviors from a carefully evolved network
Neural Computation
Proceedings of the 25th international conference on Machine learning
A BIOLOGICALLY INSPIRED METHOD FOR CONCEPTUAL IMITATION USING REINFORCEMENT LEARNING
Applied Artificial Intelligence
Hierarchical reinforcement learning with the MAXQ value function decomposition
Journal of Artificial Intelligence Research
Infinite-horizon policy-gradient estimation
Journal of Artificial Intelligence Research
Fitness landscape analysis and memetic algorithms for the quadratic assignment problem
IEEE Transactions on Evolutionary Computation
Meta-Lamarckian learning in memetic algorithms
IEEE Transactions on Evolutionary Computation
Evolutionary optimization in uncertain environments-a survey
IEEE Transactions on Evolutionary Computation
A tutorial for competent memetic algorithms: model, taxonomy, and design issues
IEEE Transactions on Evolutionary Computation
A game-theoretic and dynamical-systems analysis of selection methods in coevolution
IEEE Transactions on Evolutionary Computation
Biasing Coevolutionary Search for Optimal Multiagent Behaviors
IEEE Transactions on Evolutionary Computation
Visual Learning by Evolutionary and Coevolutionary Feature Synthesis
IEEE Transactions on Evolutionary Computation
Measuring Generalization Performance in Coevolutionary Learning
IEEE Transactions on Evolutionary Computation
Evolution of homing navigation in a real mobile robot
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Coevolving Memetic Algorithms: A Review and Progress Report
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Learning in behavior-based multi-robot systems: policies, models, and other agents
Cognitive Systems Research
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Designing an intelligent situated agent is a difficult task because the designer must see the problem from the agent's viewpoint, considering all its sensors, actuators, and computation systems. In this paper, we introduce a bio-inspired hybridization of reinforcement learning, cooperative co-evolution, and a cultural-inspired memetic algorithm for the automatic development of behavior-based agents. Reinforcement learning is responsible for the individual-level adaptation. Cooperative co-evolution performs at the population level and provides basic decision-making modules for the reinforcement-learning procedure. The culture-based memetic algorithm, which is a new computational interpretation of the meme metaphor, increases the lifetime performance of agents by sharing learning experiences between all agents in the society. In this paper, the design problem is decomposed into two different parts: 1) developing a repertoire of behavior modules and 2) organizing them in the agent's architecture. Our proposed cooperative co-evolutionary approach solves the first problem by evolving behavior modules in their separate genetic pools. We address the problem of relating the fitness of the agent to the fitness of behavior modules by proposing two fitness sharing mechanisms, namely uniform and value-based fitness sharing mechanisms. The organization of behavior modules in the architecture is determined by our structure learning method. A mathematical formulation is provided that shows how to decompose the value of the structure into simpler components. These values are estimated during learning and are used to find the organization of behavior modules during the agent's lifetime. To accelerate the learning process, we introduce a culturebased method based on our new interpretation of the meme metaphor. Our proposed memetic algorithm is a mechanism for sharing learned structures among agents in the society. Lifetime performance of the agent, which is quite important for realworld applications, increases considerably when the memetic algorithm is in action. Finally, we apply our methods to two benchmark problems: an abstract problem and a decentralized multirobot object-lifting task, and we achieve human-competitive architecture designs.