Learn More
Safe state abstraction in reinforcement learning allows an agent to ignore aspects of its current state that are irrelevant to its current decision, and therefore speeds up dynamic programming and learning. This paper explores safe state abstraction in hierarchical reinforcement learning, where learned behaviors must conform to a given partial, hierarchical(More)
– The design (synthesis) of analog electrical circuits starts with a high-level statement of the circuit's desired behavior and requires creating a circuit that satisfies the specified design goals. Analog circuit synthesis entails the creation of both the topology and the sizing (numerical values) of all of the circuit's components. The difficulty of the(More)
– It would be desirable if computers could solve problems without the need for a human to write the detailed programmatic steps. That is, it would be desirable to have a domain-independent automatic programming technique in which "What You Want Is What You Get" ("WYWIWYG" – pronounced "wow-eee-wig"). Genetic programming is such a technique. This paper(More)
An essential component of an intelligent agent is the ability to observe, encode, and use information about its environment. Traditional approaches to Genetic Programming have focused on evolving functional or reactive programs with only a minimal use of state. This paper presents an approach for investigating the evolution of learning, planning, and memory(More)
Rule-based systems used for Optical Character Recognition (OCR) are notoriously difficult to write, maintain, and upgrade. This paper describes a method for using Genetic Programming (GP) to evolve and upgrade rules for an OCR system. The language of the evolved programs was designed such that human hand-coded rules can be included into the initial(More)