Shifting Inductive Bias with Success-Story Algorithm, Adaptive Levin Search, and Incremental Self-Improvement
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Summary
In inductive transfer case studies, task sequences that allow for speeding up the learner's average reward intake through appropriate shifts of inductive bias are studied through the use of the “success-story algorithm” (SSA).
- Type
- article
- Published
- 1997-07-01
- Cited by
- 192
- References
- 63
- Access
- Open access
- OpenAlex
- https://openalex.org/W1486056878
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:14513370
Keywords
Inductive bias, Computer science, Reinforcement learning, Backtracking, Undo
References
- Theory Formation by Heuristic Search
- Book Review: An introduction to Kolmogorov Complexity and its Applications Second Edition, 1997 by Ming Li and Paul Vitanyi (Springer (Graduate Text Series))
- A Representation for the Adaptive Generation of Simple Sequential Programs
- Solving POMDPs with Levin Search and EIRA
- Stochastic Systems: Estimation, Identification, and Adaptive Control
- Genetic evolution and co-evolution of computer programs
- Reinforcement Learning for Robots Using Neural Networks
- Shift of bias for inductive concept learning
- An Introduction to Kolmogorov Complexity and Its Applications
- Reinforcement Learning with Perceptual Aliasing: The Perceptual Distinctions Approach
- Learning in embedded systems
- Adding Temporary Memory to ZCS
- Three approaches to the quantitative definition of information
- Deliberation Scheduling for Problem Solving in Time-Constrained Environments
- PALO: A Probabilistic Hill-Climbing Algorithm
- Randomness Conservation Inequalities; Information and Independence in Mathematical Theories
- Active Perception and Reinforcement Learning
- Learning Automata - A Survey
- Bandit Problems, Sequential Allocation of Experiments
- A Survey of Transfer Between Connectionist Networks
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