David Andre

A Parallel Implementation Of (2004)

This paper describes the parallel implementation of genetic programming in the C programming language using a PC 486 type computer running Windows acting as a host and a network of transputers acting...

r ELSEVIER Robotics and Autonomous Systems 22 (1997) 151-158 (2003)

This paper describes the successful parallel implementation of genetic programming on a network of processing nodes using the transputer architecture. With this approach, researchers of genetic...

Illah R. Nourbakhsh, David Andre, Carlo Tomasi, Michael R. Genesereth

A critical challenge in the creation of autonomous mobile robots is the reliable detection of moving and static obstacles.In this paper, we present a passive vision system that recovers coarse depth...

Paper TEC 26 -- Version 4 - Submitted June 12, 1997 to IEEE Transactions on Evolutionary Computation. (2003)

John R. Koza, Forrest H Bennett Iii, David Andre, Martin A

The design (synthesis) of analog electrical circuits starts with a highlevel statement of the circuit's desired behavior and requires creating a circuit that satisfies the specified design goals....

Use of Architecture-Altering Operations to Dynamically (2003)

John R. Koza, Forrest H Bennett Iii, Jason Lohn, Frank Dunlap, Martin A. Keane, David Andre

The problem of source identification involves correctly classifying an incoming signal into a category that identifies the signal's source.

State Abstraction for Programmable Reinforcement Learning Agents (2002)

Representations for Learning Control Policies (2002)

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...

State Abstraction for Programmable Reinforcement Learning Agents (2002)

Representing the expected reward or cost for taking an action in a stochastic control problem, such as automated driving, is not trivial when the state and action spaces are continuous.

Use of Architecture-Altering Operations to Dynamically (2002)

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...

John R. Koza, Forrest H Bennett Iii, Jason Lohn, Frank Dunlap, Martin A. Keane, David Andre

The problem of source identification involves correctly classifying an incoming signal into a category that identifies the signal's source.

Staff Scheduling for Inbound Call Centers and Customer Contact Centers (2002)

Alex Fukunaga, Jason Fama, David Andre, Ofer Matan, Illah Nourbakhsh

The staff scheduling problem is a critical problem in the call center (or more generally, customer contact center) industry.

Staff Scheduling for Inbound Call Centers and Customer Contact Centers (2002)

Alex Fukunaga, Jason Fama, David Andre, Ofer Matan, Illah Nourbakhsh

The staff scheduling problem is a critical problem in the call center (or more generally, customer contact center) industry. This paper describes Director, a staff scheduling system for contact...

State Abstraction for Programmable Reinforcement Learning Agents (2001)

Programmable Reinforcement Learning Agents (2001)

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...

State Abstraction for Programmable Reinforcement Learning Agents (2001)

We present an expressive agent design language for reinforcement learning that allows the user to constrain the policies considered by the learning process.The language includes standard features...

Programmable Reinforcement Learning Agents (2001)

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...

Genetic Programming: Biologically Inspired Computation that Creatively Solves Non-Trivial Problems (2000)

We present an expressive agent design language for reinforcement learning that allows the user to constrain the policies considered by the learning process.The language includes standard features...

John R. Koza, Forrest H. Bennett Iii, David Andre, Martin A. Keane

This paper describes a biologically inspired domain-independent technique, called genetic programming, that automatically creates computer programs to solve problems. Starting with a primordial ooze...

Practical Reinforcement Learning in Continuous Domains (2000)

Real-Time Reinforcement Learning in Continuous Domains (2000)

Many real-world domains have continuous features and actions, whereas the majority of results in the reinforcement learning community are for nite Markov decision processes. Much of the work that...

Programmable Reinforcement Learning Agents (2000)

this paper is the standard Markov Decision Process (MDP) setup for reinforcement learning (Kaelbling & Moore 1996). We assume that at each point in time the environment is in some state s. At each...

Real-Time Reinforcement Learning in Continuous Domains (2000)

We present an expressive agent design language for reinforcement learning that allows the user to constrain the policies considered by the learning process by partially specifying a program for the...

Generalized Prioritized Sweeping (2000)

this paper is the standard Markov Decision Process (MDP) setup for reinforcement learning (Kaelbling & Moore 1996). We assume that at each point in time the environment is in some state s. At each...

Evolving Team Darwin United (1999)

Prioritized sweeping is a model-based reinforcement learning method that attempts to focus an agent's limited computational resources to achieve a good estimate of the value of environment states. To...

David Andre, Astro Teller

. The RoboCup simulator competition is one of the most challenging international proving grounds for contemporary AI research. Exactly because of the high level of complexity and a lack of reliable...

Darwinian Programming and Engineering Design Using Genetic Programming (1999)

Forrest H Bennett Iii, John R. Koza, Martin A. Keane, David Andre

One of the central challenges of computer science is to build a system that can automatically create computer programs that are competitive with those produced by humans. This paper presents a...

Genetic Programming: Biologically Inspired Computation that Exhibits Creativity in Solving Non-Trivial Problems (1999)

Forrest H Bennett Iii, John R. Koza, Martin A. Keane, David Andre

This paper describes a biologically inspired domain-independent technique, called genetic programming, that automatically creates computer programs to solve problems. We argue that the field of...

Reinforcement learning systems are often concerned with balancing exploration of untested actions against exploitation of actions that are known to be good. The benefit of exploration can be...

Reinforcement learning systems are often concerned with balancing exploration of untested actions against exploitation of actions that are known to be good. The benefit of exploration can be...

Reinforcement learning systems are often concerned with balancing exploration of untested actions against exploitation of actions that are known to be good. The benefit of exploration can be...

Generalized Prioritized Sweeping (1999)

Reinforcement learning systems are often concerned with balancing exploration of untested actions against exploitation of actions that are known to be good. The benefit of exploration can be...

Generalized Prioritized Sweeping (1999)

Prioritized sweeping is a model-based reinforcement learning method that attempts to focus an agent's limited computational resources to achieve a good estimate of the value of environment states. To...

Practical Reinforcement Learning in Continuous Domains (1999)

Prioritized sweeping is a model-based reinforcement learning method that attempts to focus an agent's limited computational resources to achieve a good estimate of the value of environment states. To...

Jeffrey Forbes, David Andre

this paper, we propose a practical architecture for model-based reinforcement learning in continuous state and action spaces that avoids the above difficulties by using an instance-based modeling...

David Andre (1998)

Design of a High-Gain Operational Amplifier and Other Circuits by Means of Genetic Programming (1998)

: In the last few years, many researchers have begun to study how to introduce hierarchy into reinforcement learning methods. Generally, this work has pushed the envelope in one of several important...

John R. Koza, David Andre, Forrest H Bennett Iii, Martin A. Keane

: This paper demonstrates that a design for a low-distortion highgain 96 decibel (64,860-to-1) operational amplifier (including both circuit topology and component sizing) can be evolved using...

Evolution of Both the Architecture and the Sequence of Work-Performing Steps of a Computer Program Using Genetic Programming with Architecture-Altering Operations (1998)

Automated Design Of Both The Topology And Sizing Of Analog Electrical Circuits Using Genetic Programming (1998)

The goal of automatic programming is to create, in an automated way, a computer program that enables a computer to solve a problem. Ideally, an automatic programming system should require that the...

John R. Koza, Forrest H Bennett Iii, David Andre, Martin A. Keane

: This paper describes an automated process for designing analog electrical circuits based on the principles of natural selection, sexual recombination, and developmental biology. The design process...

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Automatic Discovery Using Genetic Programming of an Unknown-Sized Detector of Protein Motifs Containing Repeatedly-Used Subexpressions (1998)

Introduction Amenability to parallelization is an appealing feature of genetic algorithms, evolutionary programming, evolution strategies, classifier systems, and genetic programming [Holland 1975;...

Submitted on August 1, 1995 for AAAI Fall Symposium on Genetic Programming in Cambirdge on November 10-- 12, 1995. (1998)

Automated methods of machine learning may be useful in discovering biologically meaningful patterns that are hidden in the rapidly growing databases of genomic and protein sequences. However, almost...

Parallel Genetic Programming on a Network of Transputers (1998)

Automated methods of machine learning may prove to be useful in discovering biologically meaningful information hidden in the rapidly growing databases of DNA sequences and protein sequences. Genetic...

Evolving Sorting Networks using Genetic Programming and the Rapidly Reconfigurable Xilinx 6216 Field-Programmable Gate Array (1998)

This paper describes the parallel implementation of genetic programming in the C programming language using a PC 486 type computer running Windows acting as a host and a network of transputers acting...

John R. Koza, Forrest H Bennett Iii, Jeffrey L. Hutchings, Stephen L. Bade, Martin A. Keane, David Andre

This paper describes how the massive parallelism of the rapidly reconfigurable Xilinx XC6216 FPGA (in conjunction with Virtual Computing Corporation's H.O.T. Works board) can be exploited to...

Evolving Sorting Networks using Genetic Programming and Rapidly Reconfigurable Field-Programmable Gate Arrays (1998)

John R. Koza, Forrest H Bennett Iii, Jeffrey L. Hutchings, Stephen L. Bade, Martin A. Keane, David Andre

This paper describes ongoing work involving the use of the Xilinx XC6216 rapidly reconfigurable field-programmable gate array to evolve sorting networks using genetic programming. We successfully...

Automatic Discovery of Protein Motifs Using Genetic Programming (1998)

Evolving Computer Programs using Rapidly Reconfigurable Field-Programmable Gate Arrays and Genetic Programming (1998)

Automated methods of machine learning may prove to be useful in discovering biologically meaningful information hidden in the rapidly growing databases of DNA sequences and protein sequences. Genetic...

John R. Koza, Forrest H Bennett Iii, Jeffrey L. Hutchings, Stephen L. Bade, Martin A. Keane, David Andre

This paper describes how the massive parallelism of the rapidly reconfigurable Xilinx XC6216 FPGA (in conjunction with Virtual Computing's H.O.T. Works board) can be exploited to accelerate the...

Classifying Proteins as Extracellular using Programmatic Motifs and Genetic Programming (1998)

John R. Koza, Forrest H Bennett Iii, David Andre

As newly sequenced proteins are deposited into the world's ever-growing archive of protein sequences, they are typically immediately tested by various computerized algorithms for clues as to their...

Using Programmatic Motifs and Genetic Programming to Classify Protein Sequences as to Cellular Location (1998)

John R. Koza, Forrest H Bennett Iii, David Andre

As newly sequenced proteins are deposited into the world's ever-growing archives, they are typically immediately tested by various algorithms for clues as to their biological structure and function....

Learning Hierarchical Behaviors (1998)

Multi-level parallelism in automatically synthesizing soccer-playing programs for Robocup using genetic programming (1998)

: In the last few years, many researchers have begun to study how to introduce hierarchy into reinforcement learning methods. Generally, this work has pushed the envelope in one of several important...

Learning and Upgrading Rules for an OCR System Using Genetic Programming (1998)

: Many of the various proposals for tomorrow's supercomputers have included clusters of multiprocessors as an essential component. However, when designing the systems of the future, it is important...

: 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...

David Andre (1998)

Astro Teller, David Andre

Introduction and Overview The problem of determining which feature set to use has always been somewhat of a dilemma in the pattern recognition field. Not only must a feature set have features that...

Astro Teller (1998)

For many problems to which genetic programming has been applied, choosing the number of fitness cases with which to evaluate the individuals is a crucial decision. If too few fitness cases are used,...

Generalized Prioritized Sweeping (1998)

Evolution of Mapmaking: Learning, planning, and memory using Genetic Programming (1998)

Prioritized sweeping is a model-based reinforcement learning method that attempt to focus the agent's limited computational resources to achieve a good estimate of the value of environment states....

The Automatic Programming of Agents that Learn Mental Models and Create Simple Plans of Action (1998)

: 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...

David Andre, Visiting Scholar

: An essential component of an intelligent agent is the ability to notice, encode, store, and utilize information about its environment. Traditional approaches to program induction have focused on...

Generalized Prioritized Sweeping (1998)

Evolution of a Tri-State Frequency Discriminator for the Source Identification Problem using Genetic Programming (1997)

Prioritized sweeping is a model-based reinforcement learning method that attempts to focus an agent's limited computational resources to achieve a good estimate of the value of environment states. To...

John R. Koza, Forrest H Bennett Iii, Jason Lohn, Frank Dunlap, Martin A. Keane, David Andre

Automated synthesis of analog electronic circuits is recognized as a difficult problem. Genetic programming was used to evolve both the topology and the sizing (numerical values) for each component...

Use of Architecture-Altering Operations to Dynamically Adapt a Three-Way Analog Source Identification Circuit to Accommodate a New Source (1997)

John R. Koza, Forrest H Bennett Iii, Jason Lohn, Frank Dunlap, Martin A. Keane, David Andre

The problem of source identification involves correctly classifying an incoming signal into a category that identifies the signal's source. The problem is difficult because information is not...

Prioritized sweeping is a model-based reinforcement learning method that attempt to focus the agent's limited computational resources to achieve a good estimate of the value of environment states....

Prioritized sweeping is a model-based reinforcement learning method that attempt to focus the agent's limited computational resources to achieve a good estimate of the value of environment states....

Prioritized sweeping is a model-based reinforcement learning method that attempt to focus the agent's limited computational resources to achieve a good estimate of the value of environment states....

Prioritized sweeping is a model-based reinforcement learning method that attempt to focus the agent's limited computational resources to achieve a good estimate of the value of environment states....

Prioritized sweeping is a model-based reinforcement learning method that attempt to focus the agent's limited computational resources to achieve a good estimate of the value of environment states....

Prioritized sweeping is a model-based reinforcement learning method that attempt to focus the agent's limited computational resources to achieve a good estimate of the value of environment states....

Prioritized sweeping is a model-based reinforcement learning method that attempt to focus the agent's limited computational resources to achieve a good estimate of the value of environment states....

Rapidly Reconfigurable Field-Programmable Gate Arrays for Accelerating Fitness Evaluation in Genetic Programming (1997)

Prioritized sweeping is a model-based reinforcement learning method that attempt to focus the agent's limited computational resources to achieve a good estimate of the value of environment states....

John R. Koza, Forrest H Bennett Iii, Jeffrey L. Hutchings, Stephen L. Bade, Martin A. Keane, David Andre

The dominant component of the computational burden of solving non-trivial problems with evolutionary algorithms is the task of measuring the fitness of each individual in each generation of the...

Classifying Protein Segments as Transmembrane Domains Using Architecture-Altering Operations in Genetic Programming (1997)

Visiting Scholar, John R. Koza, David Andre

Introduction and Overview The goal of automatic programming is to create, in an automated way, a computer program that enables a computer to solve a problem. Ideally, an automatic programming system...

Automatic Programming of a Time-Optimal Robot Controller and an Analog Electrical Circuit to Implement the Robot Controller by Means of Genetic Programming (1997)

John R. Koza, Forrest H Bennett, Visiting Scholar, Martin A. Keane, David Andre

Genetic programming is an automatic programming technique that evolves computer programs to solve, or approximately solve, problems. This paper presents two examples in which genetic programming...

Evolution of a Time-Optimal Fly-To Controller Circuit using Genetic Programming (1997)

John R. Koza, Forrest H Bennett, Visiting Scholar, Martin A. Keane, David Andre

Most problem-solving techniques used by engineers involve the introduction of analytical and mathematical representations and techniques that are entirely foreign to the problem at hand. Genetic...

Evolution of Iteration in Genetic Programming (1997)