UW Contributions:Past and Future

Martin V. ButzDepartment of Cognitive Psychology

University of Würzburg, Germanyhttp://www-illigal.ge.uiuc.edu/~butz

mbutz@psychologie.uni-wuerburg.de

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Overview

1. Publications2. Dissemination work

1. Organization of ABiALS 2006 during SAB with ISTC-CNR2. Workshop proceedings CD, Postworkshop book with additional overview papers

3. Work on multiple facets1. Continued work on the XCS classifier system for function approximation –

hyperellipsoidal conditions, RLS, and compaction2. Visuomotor grounded tracking system3. The SURE_REACH architecture: A Sensorimotor Unsupervised Redundancy

Resolving control Architecture 4. Collaborations

1. OFAI with XCS / AIS comparisons2. ISTC-CNR with ideomotor principle & TOTE - related system comparisons3. Tracking and object-recognition experiments with IDSIA (+ LUND ?)4. Integration of SURE_REACH architecture with RL component from ISTC-CNR

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PublicationsBook:Butz, M.V. (2006)

Rule-based evolutionary online learning systems: A principled approach to LCS analysis and design. Studies in Fuzziness and Soft Computing Series, Springer Verlag, Berlin-Heidelberg, Germany.

Journals:Butz, M.V., Goldberg, D.E., Lanzi, P.L., & Sastry, K. (in press). Problem Solution Sustenance in XCS: Markov Chain

Analysis of Niche Support Distributions and Consequent Computational Complexity. Genetic Programming and Evolvable Machines.

Butz, M.V., Pelikan, M., Llorà, X., & Goldberg, D.E. (in press) Automated global structure extraction for effective local building block processing in XCS. Evolutionary Computation Journal.

Butz, M.V., Herbort, O., & Hoffmann, J. (submitted) Exploiting Redundancy for Flexible Behavior: Unsupervised Learning of a Modular Sensorimotor Control Architecture

Butz, M.V., Lanzi, P.L., & Wilson, S.W. (submitted) Function Approximation with XCS: Hyperellipsoidal Conditions, Recursive Least Squares, and Compaction. IEEE Transactions on Systems Man and Cybernetics, Part B.

Conferences:Butz, M.V., Pelikan, M. (2006). Studying XCS/BOA learning in Boolean functions: Structure encoding and random

boolean functions. Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2006). 1449-1456.

Butz, M.V., Lanzi, P. L., Wilson, S. W. (2006). Hyper-ellipsoidal conditions in XCS: Rotation, linear approximation, and solution structure. Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2006). 1457-1464.

Workshops:Pezzulo, G., Baldassarre, G., Butz, M.V., Castelfranchi, C., & Hoffmann, J. (2006) An Analysis of the Ideomotor

Principle and TOTE. ABiALS 2006.Herbort, O., & Butz, M.V. (2006). Unsupervised Learning of Inverse Dynamics Model. CogSys II.

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Dissemination

• … apart from publications: • Multiple invited presentations on XCS and Anticipations• Organization of ABiALS 2006 with ISTC-CNR:

Anticipatory Behavior in Adaptive Learning Systems• Workshop CD• Post-workshop proceedings book

Submission deadline, 30th of November

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Work on XCSF for Function Approximation

• XCSF is a partially overlapping, piecewise linear function approximation systems– Learns iteratively, online– Clusters the space to yield accurate function approximations

• Improvements:– Rotation improves / speeds-up evolutionary process– RLS makes approximations more accurate– Compaction for generalized function approximation representation.

• Results:– Effective function approximation – max problem is 7D (sin(2 PI (x1+…+x7))– Noise robustness – N(0,.1) noise – best performance compared to results

in ICML 2004– Generalization: Comparable results to Atkeson, Schaal (1998) Neural

Computation with heuristic approach.

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Visuomotor-grounded Tracking System

Goal: Object recognition by analysis of observed visual (object-related) flow.

Currently integrated in IKAROSApproach:1. Learn about optical flow observing visual changes caused by

own actions (retinal tracking movement)1. Represent the flow by local, sigma-pi predictors2. Integrate population encoding of motor action, current perception to

generate future perception2. Observe flow deriving local movement vectors3. Use the movement information to differentiate types of objects4. Use the knowledge to predict behavior of object

1. Bouncing behavior2. Object permanence

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The SURE_REACH Architecture

Anticipatory, goal-oriented robot arm approachCovers two spatial representations with population encoding:

End-point coordinate spacePosture space

Associates End-point and posture space.Learns associative, action-dependent inverse modelsReaches goals flexibly and accurately:

Choosing the shortest path if possible.Flexibly obeying additional goal constraints.Avoiding obstacles by an effective inhibition of neurons

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Collaborations

1. OFAI with XCS / AIS comparisons – test in robot arm scenario

2. ISTC-CNR with ideomotor principle & TOTE - related system comparisons

3. Tracking and object-recognition experiments with IDSIA (+ LUND ?)

4. Integration of SURE_REACH architecture with RL component from ISTC-CNR (?)

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Deliverable 4.2

• Due date: Month 30 – that is, 03/2007• Description: Results of tests of architectures which model

mechanisms based on analogy, proactive and goal directed behaviour. The deliverable will be a report that illustrates the results of the tests of different architectures and of at least one robot and a comparison of their performances in the implemented scenario selected for analysing the cognitive functions set of Goal directed behaviour, Pro-activity and Analogy. Also a description of the implemented architectures and of the robot will be given.

• Milestone 4.2. Final results concerning measures of performance of improved architectures and comparison of performances.We will provide final results concerning measures of performance of at least two improved architectures in the scenario selected for analyzing the cognitive functions set of Goal directed behavior, Pro-activity and Analogy. At least one of the improved architectures will be a real Robot, the other ones will be simulated using agent software. Moreover, a comparison among the performances obtained by each architecture in the scenario will be provided.

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For Deliverable 4.2

• Who is doing system comparisons?• On which scenario can we test the systems for

comparison?• Which real robot are we going to use for comparison?