Proceedings of the seventh international conference (1990) on Machine learning
Learning to Perceive and Act by Trial and Error
Machine Learning
Reinforcement learning for the adaptive control of perception and action
Reinforcement learning for the adaptive control of perception and action
Reinforcement learning with hidden states
Proceedings of the second international conference on From animals to animats 2 : simulation of adaptive behavior: simulation of adaptive behavior
Training agents to perform sequential behavior
Adaptive Behavior
Memoryless policies: theoretical limitations and practical results
SAB94 Proceedings of the third international conference on Simulation of adaptive behavior : from animals to animats 3: from animals to animats 3
Adding temporary memory to ZCS
Adaptive Behavior
Evolutionary Robotics: The Biology,Intelligence,and Technology
Evolutionary Robotics: The Biology,Intelligence,and Technology
Iterated Prisoner's Dilemma with Choice and Refusal of Partners: Evolutionary Results
Proceedings of the Third European Conference on Advances in Artificial Life
Intelligence Without Reason
Reinforcement learning with selective perception and hidden state
Reinforcement learning with selective perception and hidden state
Evolutionary Computation - Special issue on magnetic algorithms
Memory analysis and significance test for agent behaviours
Proceedings of the 8th annual conference on Genetic and evolutionary computation
A quantitative analysis of memory requirement and generalization performance for robotic tasks
Proceedings of the 9th annual conference on Genetic and evolutionary computation
| Hi-index | 0.00 |
The Woods Problem is a difficult problem for purely reactive systems to handle. The difficulties are related to the perceptual aliasing problem, and the use of internal memory has been suggested to solve the problem. In this paper a novel approach in evolutionary computation is introduced to quantify the amount of memory required for a given task. The approach has been applied to Woods Problems such as wood101, woods102, Sutton's gridworld and woods14.Finite state machine controllers are used, as these permit easy measurement of the amount of memory in the controller. A concurrent evolutionary search for-the minimal but optimal control structure in memory-based systems, using an evolutionary Pareto-optimal search mechanism, determines the best behavior fitness for each level of controller memory. This memory analysis demonstrates the effect of internal memory in evolved controllers for Woods Problems and is also used to investigate the relationship between the number of sensors available to an agent and the amount of memory necessary for effective behavior.