iBet uBet web content aggregator. Adding the entire web to your favor.
iBet uBet web content aggregator. Adding the entire web to your favor.



Link to original content: https://doi.org/10.1023/A:1006567623867
Connectionist Learning in Behaviour-Based Mobile Robots: A Survey | Artificial Intelligence Review Skip to main content
Springer Nature Link
Log in

Connectionist Learning in Behaviour-Based Mobile Robots: A Survey

  • Published:
Artificial Intelligence Review Aims and scope Submit manuscript

Abstract

This paper is a survey of some recentconnectionist approaches to the design and developmentof behaviour-based mobile robots. The research isanalysed in terms of principal connectionist learningmethods and neurological modeling trends. Possibleadvantages over conventionally programmed methods areconsidered and the connectionist achievements to dateare assessed. A realistic view is taken of theprospects for medium term progress and someobservations are made concerning the direction thismight profitably take.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  • Ackley, D. H. & Littman, M. L. (1990). Generalization and Scaling in Reinforcement Learning. In Touretsky, D. S. (ed.) Advances in Neural Information Processing Systems 2, 550–557. Morgan Kaufman: San Mateo, CA.

    Google Scholar 

  • Almassy, N. & Verschure, P. (1992). Optimizing Self-Organizing Control Architectures with Genetic Algorithms: The Interaction Between Natural Selection and Ontogenesis. Proceedings of the Second Conference on Parallel Problem Solving from Nature, 451–460. Elsevier.

  • Barto, A. G. (1990a). Connectionist Learning for Control: An Overview. In Miller, W. T., Sutton, R. S. & Werbos, P. J. (eds.) Neural Networks for Control. MIT Press: Cambridge, MA.

    Google Scholar 

  • Barto, A. G. (1990b). Some Learning Tasks from a Control Perspective. COINS Technical Report 90122, Computer and Information Science Department: University of Massachusetts.

    Google Scholar 

  • Beer, D., Chiel, H. J. & Sterling, L. S. (1990). A Biological Perspective on Autonomous Agent Design. In Maes, P. (ed.) Designing Autonomous Agents, 169–186. MIT Press.

  • Brooks, R. A. (1986). A Robust Layered Control System for a Mobile Robot. IEEE Journal of Robotics and Automation: 14–23.

  • Brooks, R. A. (1991). Intelligence Without Representation. Artificial Intelligence 47: 139–159.

    Google Scholar 

  • Brooks, R. A. (1994). Coherent Behaviour from Many Adaptive Processes. In From Animals to Animats 3: Proceedings of the Third International Conference on the Simulation of Adaptive Behavior, 22–29. MIT Press.

  • Brooks, R. A. & Mataric, M. J. (1993). Real Robots, Real Learning Problems. In Connell, J. H. & Mahadevan, S. (eds.) Robot Learning, 193–213. Kluwer Academic Publishers.

  • Buhlmeier, A. (1994). Conditioning and Robot Control. Proceedings of the International Conference on Artificial Neural Networks, 1283–1286. Springer-Verlag.

  • Chandrasekeran, B. & Josephson, S. G. (1993). Architecture of Intelligence: The Problems and Current Approaches to Solutions. Current Science 64(6).

  • Chesters, W. & Hayes, G. (1994). Connectionist Environment Modelling in a Real Robot. From Animals to Animats 3: Proceedings of the Third International Conference on the Simulation of Adaptive Behaviour, 189–187. MIT Press.

  • Connell, J. H. & Mahadevan, S. (1993). Introduction to Robot Learning. In Connell J. H. & Mahadevan, S. (eds.) Robot Learning, 1–17. Kluwer Academic Publishers.

  • Copeland, J. (1993). Artificial Intelligence: A Philosophical Introduction. Blackwell: Oxford.

    Google Scholar 

  • Elman, J. L. (1990). Finding Structures in Time. Cognitive Science 14: 179–212.

    Google Scholar 

  • Fagg, A. H., Lotspeich, D. & Beckey, G. A. (1994). A Reinforcement Learning Approach to Reactive Control Policy Design for Autonomous Robots and Automation. Proceedings of the IEEE Conference on Robotics.

  • Franchi, P., Morasso & Vercelli, G. (1994). A Hybrid Self-Organizing Architecture for Autonomous Mobile Robots. Proceedings of the International Conference on Artificial Neural Networks, 1283–1286.

  • Gaussier, P. & Zrehen, S. (1994). A Topological Neural Map for On-Line Learning. From Animals to Animats 3: Proceedings of the Third International Conference on the Simulation of Adaptive Behaviour, 275–281. MIT Press.

  • Hallam, B., Halperin, J. R. P. & Hallam, J. C. T. (1994). An Ethological Model of Learning and Motivation for Implementation in Mobile Robots, Research Paper 629. Department of AI: Edinburgh University.

    Google Scholar 

  • Hartson, C. (1990). Application of Neural Networks to Robotics. In Maren, A., Hartson C. & Pap, R. (eds.) Handbook of Neural Computing Applications, 381–391. Academic Press.

  • Harvey, I., Husbands, P. & Cliff, D. (1993). Issues in Evolutionary Robotics. From Animals to Animats 2: Proceedings of the Second International Conference on the Simulation of Adaptive Behaviour, 364–373. MIT Press.

  • Haykin, S. (1994). Neural Networks: A Comprehensive Introduction. Macmillan, New York.

    Google Scholar 

  • Jordon, M. I. & Jacobs, R. A. (1992). Hierarchies of Adaptive Experts. In Moody, J. E., Hanson, S. J. & Lippmann, R. P. (eds.) Advances in Neural Information Processing Systems 4, 985–992. Morgan Kaufmann.

  • Khatib, O. (1986). Real-Time Obstacle Avoidance for Manipulators and Mobile Robots. International Journal of Robotics Research 5(1): 90–98.

    Google Scholar 

  • Kohonen, T. (1982). Self-Organized Formation of Topographically Correct Feature Maps. Biological Cybernetics 43: 59–69.

    Google Scholar 

  • Lin, L. J. (1991). Programming Robots Using Reinforcement Learning and Teaching. Proceedings of AAAI91: 781–786.

  • Lin, L. J. (1993). Hierarchical Learning of Robot Skills by Reinforcement. Proceedings of IEEE Conference on Neural Networks: 181–187.

  • MacFarland, D. & Bösser, T. (1993). Intelligent Behaviour in Animals and Robots. MIT Press.

  • Maes, P. (1990). Situated Agents Can Have Goals. Robotics and Autonomous Systems 6: 49–70.

    Google Scholar 

  • Maes, P. (1995). Modeling Adaptive Autonomous Agents. In Langton, C. G. (ed.) Artificial Life: An Overview, 135–162. MIT Press.

  • Maes, P. & Brooks, R. A. (1990). Learning to Coordinate Behaviours. In Proceedings of AAAI90, 796–802.

  • Mahadevan, J. H. & Connell, J. (1992). Automatic Programming of Behaviour-Based Robots Using Reinforcement Learning. Artificial Intelligence 55: 311–365.

    Google Scholar 

  • Mataric, M. J. (1990). A Distributed Model for Mobile Robot Environment Learning and Navigation. Technical Report 1228, AI Lab, MIT.

  • Meeden, L., Mcgraw, G. & Blank, D. (1993). Emergent Control and Planning in an Autonomous Vehicle. Proceedings of the Fifteenth Annual Meeting of the Cognitive Science Society.

  • Millan, J. & Del R. (1994). Learning Efficient Reactive Behavioural Sequences from Basic Reflexes.

  • Nehmzow, U. (1995). Flexible Control of Mobile Robots Through Autonomous Competence Acquisition. Measurement and Control 28: 48–54.

    Google Scholar 

  • Nehmzow, U., Hallam, J. & Smithers, T. (1989). Really Useful Robots. In Kanade, T., Groen, F. C. A. & Hertzberger, L. O. (eds.) Intelligent Autonomous Systems 2. Elsevier: Amsterdam.

    Google Scholar 

  • Nehmzow, U. & McGonigle, B. (1994). Achieving Rapid Adaptations in Robots by Means of External Tuition. From Animals to Animats 3: Proceedings of the Third International Conference on the Simulation of Adaptive Behaviour, 301–308. MIT Press.

  • Nehmzow, U., Smithers, T & McGonigle, B. (1993). Increasing Behavioural Repertoire in a Mobile Robot. In Meyer, J. A., Roitblat, H. & Wilson, S. (eds). From Animals to Animats 2, Proceedings of the Second International Conference on the Simulation of Adaptive Behaviour, 291–297. MIT Press.

  • Pfeifer, R. & Scheier C. (1994). From Perception to Action: The Right Direction? In: Gaussier, P. & Nicoud, J. D. (eds.) From Perception to Action, 1–11. IEEE Computer Society Press.

  • Raphael, B. (1976). The Robot ‘Shakey’ and ‘His’ Successors. Computers and People 25(10): 7.

    Google Scholar 

  • Rutkowska, J. C. (1994). Emergent Functionality in Human Infants. From Animals to Animats 3: Proceedings of the Third International Conference on the Simulation of Adaptive Behaviour, 178–188. MIT Press.

  • Rumelhart, D. E., Hinton, G. E. & Williams, R. J. (1986). Learning Internal Representations by Error Propagation. In Rumelhart, D. E. & McLelland, J. (eds.) Parallel Distributed Processing: Explorations in the Microstructure of Cognition, Bradford Books/MIT Press: Cambridge, MA.

    Google Scholar 

  • Rylatt, R. M., Czarnecki, C. A. & Routen, T. W. (1995). A Perspective on the Future of Behaviour-Based Robotics. Mobile Robots Workshop Notes, Tenth Biennial Conference on Artificial Intelligence and Simulated Behaviour. Sheffield, England.

    Google Scholar 

  • Saunders, G. M., Kolen, J. F. & Pollack, J. B. (1994). The Importance of Leaky Levels for Behaviour-Based AI. From Animals to Animats 3: Proceedings of the Third International Conference on the Simulation of Adaptive Behaviour, 275–281. MIT Press.

  • Scutt, T. (1994). The Five Neuron Trick. From Animals to Animats 3: Proceedings of the Third International Conference on the Simulation of Adaptive Behaviour, 365–369. MIT Press.

  • Sutton, R. S. (1988). Learning to Predict by the Methods of Temporal Differences. Machine Learning 3: 9–44.

    Google Scholar 

  • Tani, J. & Fukumura, N. (1994). Learning Goal-Directed Sensory-Based Navigation of a Mobile Robot. Neural Networks 7(3): 553–563.

    Google Scholar 

  • Thrun, S. (1994). A Lifelong Learning Perspective for Mobile Robot Control. Proceedings of the IEEE Conference on Intelligent Robots and Systems, 12–16.

  • Verschure, P. F. M. J. & Pfeifer, R. (1993). Categorisation, Representations and the Dynamics of System-Environment Interaction: A Case Study in Autonomous Systems. From Animals to Animats 2: Proceedings of the Second International Conference on the Simulation of Adaptive Behaviour, 210–217. MIT Press.

  • Van de Velde, W. (ed.) (1993). Toward Learning Robots. Bradford Books: MIT Press, Cambridge, MA.

    Google Scholar 

  • Verschure, P. F. M. J. (1992). Taking Connectionism Seriously: The Vague Promise of Subsymbolism and an Alternative. Proceedings of the Fourteenth Annual Conference of the Cognitive Science Society. Erlbaum: N. Y.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science, De Montfort University, Leicester, LE1 9BH, U.K.

    Mark Rylatt, Chris Czarnecki & Tom Routen

Authors
  1. Mark Rylatt
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Chris Czarnecki
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Tom Routen
    View author publications

    You can also search for this author in PubMed Google Scholar

Rights and permissions

Reprints and permissions

About this article

Cite this article

Rylatt, M., Czarnecki, C. & Routen, T. Connectionist Learning in Behaviour-Based Mobile Robots: A Survey. Artificial Intelligence Review 12, 445–468 (1998). https://doi.org/10.1023/A:1006567623867

Download citation

  • Issue Date:

  • DOI: https://doi.org/10.1023/A:1006567623867

Search

Navigation