A Doubly Layered, Genetic Penetrance Learning System

نویسنده

  • Larry A. Rendell
چکیده

The author’s original state-space learning system (based on a probabilistic performance measure clustered in feature space) was effective in optimizing parameterized linear evaluation functions. However, more accurate probability estimates would allow stabilization in cases of strong feature interactions. To attain this accuracy and stability, a second level of learning is added, a genetic (parallel) algorithm which supervises multiple activations of the original system. This scheme is aided by the probability clusters themselves. These structures are intermediate between the detailed performance statistics and the more general heuristic, and they estimate an absolute quantity independently of one another. Consequently the system allows both credit localization at this mediating level of knowledge and feature interaction at the derived heur istr level. Earlv experimental results have been encouraging. “As predicted by the analysis, stability is very good.

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

TJP2 Gene Mutation c.G1012A May Responsible for Congenital Hearing Loss with Incomplete Penetrance in An Iranian Pedigree

Hereditary hearing loss (HHL) comprises half of the congenital deafness which arises from genetic mutations. Mutations in the TJP2 gene, encoding tight junction protein 2, are one of the gene alterations in HHL resulting in an autosomal dominant nonsyndromic form of the disease. An 11-year-old male patient with clinically approved congenital hearing loss was referred to our laboratory....

متن کامل

Layered Learning in Genetic Programming for a Cooperative Robot Soccer Problem

We present an alternative to standard genetic programming (GP) that applies layered learning techniques to decompose a problem. GP is applied to subproblems sequentially, where the population in the last generation of a subproblem is used as the initial population of the next subproblem. This method is applied to evolve agents to play keepaway soccer, a subproblem of robotic soccer that require...

متن کامل

Layered Learning for Evolving Goal Scoring Behavior in Soccer Players

Layered learning allows decomposition of the stages of learning in a problem domain and has many positive effects, both on the amount of computation required for learning and for program comprehension and verification. We apply this technique to the evolution of goal scoring behavior in soccer players and show that layered learning is on average able to find solutions comparable to standard gen...

متن کامل

Genetic Programming And Multi-agent Layered Learning By Reinforcements

We present an adaptation of the standard genetic program (GP) to hierarchically decomposable, multi-agent learning problems. To break down a problem that requires cooperation of multiple agents, we use the team objective function to derive a simpler, intermediate objective function for pairs of cooperating agents. We apply GP to optimize first for the intermediate, then for the team objective f...

متن کامل

Genetic Programming for Layered Learning of Multi-agent Tasks

We present an adaptation of the standard genetic program (GP) t o hierarchically decomposable, multi-agent learning problems. To break down a problem that requires cooperation of multiple agents, we use the team objective function to derive a simpler, intermediate objective function for pairs of cooperating agents. W e apply GP to optimize first for the intermediate, then for the team objective...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 1983