The structure-mapping engine: algorithm and examples
Artificial Intelligence
EXCALIBUR: a program for planning and reasoning with processes
Artificial Intelligence
CYC: a large-scale investment in knowledge infrastructure
Communications of the ACM
Planning and Learning by Analogical Reasoning
Planning and Learning by Analogical Reasoning
Learning to Predict by the Methods of Temporal Differences
Machine Learning
SHOP: Simple Hierarchical Ordered Planner
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
Companion cognitive systems: a step toward human-level AI
AI Magazine - Special issue on achieving human-level AI through integrated systems and research
Value-function-based transfer for reinforcement learning using structure mapping
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Reinforcement learning: a survey
Journal of Artificial Intelligence Research
The virtue of reward: performance, reinforcement and discovery in case-based reasoning
ICCBR'05 Proceedings of the 6th international conference on Case-Based Reasoning Research and Development
Soar-RL: integrating reinforcement learning with Soar
Cognitive Systems Research
Case-Based Reasoning in Transfer Learning
ICCBR '09 Proceedings of the 8th International Conference on Case-Based Reasoning: Case-Based Reasoning Research and Development
Analogical model formulation for transfer learning in AP Physics
Artificial Intelligence
Syntactic principles of heuristic-driven theory projection
Cognitive Systems Research
Exploiting persistent mappings in cross-domain analogical learning of physical domains
Artificial Intelligence
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A key problem in playing strategy games is learning how to allocate resources effectively. This can be a difficult task for machine learning when the connections between actions and goal outputs are indirect and complex. We show how a combination of structural analogy, experimentation, and qualitative modeling can be used to improve performance in optimizing food production in a strategy game. Experimentation bootstraps a case library and drives variation, while analogical reasoning supports retrieval and transfer. A qualitative model serves as a partial domain theory to support adaptation and credit assignment. Together, these techniques can enable a system to learn the effects of its actions, the ranges of quantities, and to apply training in one city to other, structurally different cities. We describe experiments demonstrating this transfer of learning.