UW Contributions: Past and Future Martin V. Butz Department of Cognitive Psychology University of Würzburg, Germany

06/15/2005Towards Hierarchical Cognitive Systems2 Overview 1.Publications 2.Dissemination work 1.Organization of ABiALS 2006 during SAB with ISTC-CNR 2.Workshop proceedings CD, Postworkshop book with additional overview papers 3.Work on multiple facets 1.Continued work on the XCS classifier system for function approximation – hyperellipsoidal conditions, RLS, and compaction 2.Visuomotor grounded tracking system 3.The SURE_REACH architecture: A Sensorimotor Unsupervised Redundancy Resolving control Architecture 4.Collaborations 1.OFAI with XCS / AIS comparisons 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

06/15/2005Towards Hierarchical Cognitive Systems3 Publications Book: 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) 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) Workshops: Pezzulo, G., Baldassarre, G., Butz, M.V., Castelfranchi, C., & Hoffmann, J. (2006) An Analysis of the Ideomotor Principle and TOTE. ABiALS Herbort, O., & Butz, M.V. (2006). Unsupervised Learning of Inverse Dynamics Model. CogSys II.

06/15/2005Towards Hierarchical Cognitive Systems4 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

06/15/2005Towards Hierarchical Cognitive Systems5 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.

06/15/2005Towards Hierarchical Cognitive Systems6 Visuomotor-grounded Tracking System Goal: Object recognition by analysis of observed visual (object- related) flow. Currently integrated in IKAROS Approach: 1.Learn about optical flow observing visual changes caused by own actions (retinal tracking movement) 1.Represent the flow by local, sigma-pi predictors 2.Integrate population encoding of motor action, current perception to generate future perception 2.Observe flow deriving local movement vectors 3.Use the movement information to differentiate types of objects 4.Use the knowledge to predict behavior of object 1.Bouncing behavior 2.Object permanence

06/15/2005Towards Hierarchical Cognitive Systems7 The SURE_REACH Architecture Anticipatory, goal-oriented robot arm approach Covers two spatial representations with population encoding: End-point coordinate space Posture space Associates End-point and posture space. Learns associative, action-dependent inverse models Reaches goals flexibly and accurately: Choosing the shortest path if possible. Flexibly obeying additional goal constraints. Avoiding obstacles by an effective inhibition of neurons

06/15/2005Towards Hierarchical Cognitive Systems8 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 (?)

06/15/2005Towards Hierarchical Cognitive Systems9 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.

06/15/2005Towards Hierarchical Cognitive Systems10 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?