Task sequence planning by intelligent agent with context awareness

: pp. 12 - 20
Lviv Polytechnic National University, Computer Engineering Department

The problem of context-aware task sequence planning for independent or weakly related tasks by an intelligent agent has been considered. The principle of matching the task to the context is analyzed. The structure of the context-aware task sequence planning module as part of an intelligent agent and the corresponding algorithm are proposed. In the structure of the planning module, three main groups of blocks are implemented: operations with tasks, operations with the context, determining the relevance of tasks to the context.

The article also proposes an algorithm for calculating the dynamic priority of a task, an algorithm for determining the relevance of a task to the context, and an algorithm for adapting a set of rules for matching the task to the context. The value of the dynamic priority depends on the static priority of the task, which is assigned when a new task is added, and the degree of correspondence of the task to the context, taking into account the context vector. Two modes of starting the planning algorithm are allowed: when the intelligent agent requests a new task and when the context vector changes. The dynamic priority calculation algorithm is performed independently for each task. As a result, its software implementation has a large parallelization resource.

To adapt the set of rules for matching tasks to the context, a scheme of reinforcement learning in a contextual multi-armed bandit was used. For each matching rule, a separate instance of the reinforcement learning procedure is performed. The reinforcement learning method used in the article is Upper-Confidence-Bound Action Selection. The functional scheme of the reinforcement learning procedure, implemented in the prototype of the context-aware task sequence planning module has been proposed. The functional scheme of the reinforcement learning procedure allows the use of data decomposition and functional decomposition to parallelize the corresponding calculations.

  1. Stuart Russell, Peter Norvig, Artificial Intelligence: A Modern Approach, 4th edition, Pearson, 2020. – 1136 p. ISBN: 978-0-13-461099-3
  2. David L. Poole, Alan K. Mackworth, Artificial Intelligence: Foundations of Computational Agents, 2nd Ed., Cambridge University Press, 2017. – 820 p. ISBN: 978-1-13-964321-4
  3. Multiagent Systems, by Gerhard Weiss (Editor), 2nd Ed., The MIT Press, 2013. – 920 p. ISBN: 978-0-26- 253387-4
  4. Tierney K., Browne J., Task sequence planning, In: Bernhardt R., Dillman R., Hörmann K., Tierney K. (eds) Integration of Robots into CIM. Springer, 1992. – pp. 36-44, ISBN: 978-0-41-237140-0
  5. Tiehua Cao, Arthur C. Sanderson, Intelligent Task Planning Using Fuzzy Petri Nets, World Scientific, 1996 – 192 p. ISBN: 978-9-81-022556-8
  6. J. Rosell, N. Munoz and A. Gambin, "Robot tasks sequence planning using Petri nets," Proceedings of the IEEE International Symposium on Assembly and Task Planning, 2003., Besancon, France, 2003, pp. 24-29, DOI: 10.1109/isatp.2003.1217182
  7. L. Zhang et al., "Adaptive quantum genetic algorithm for task sequence planning of complex assembly systems," in Electronics Letters, vol. 54, no. 14, 2018. – pp. 870-872, DOI: 10.1049/el.2018.0609
  8. Schilit B., Adams N., Want R., Context-aware computing applications, in Proceedings of the IEEE Workshop on “Mobile Computing Systems and Applications”, IEEE Computer Society, 1994. – pp. 85-90, DOI: 10.1109/wmcsa.1994.16
  9. Abowd G.D., Dey A.K., Brown P.J., Davies N., Smith M., Steggles P. (1999) Towards a Better Understanding of Context and Context-Awareness. In: Gellersen HW. (eds) Handheld and Ubiquitous Computing. HUC 1999. Lecture Notes in Computer Science, vol 1707. Springer, Berlin, Heidelberg. – pp. 304-307, DOI: 10.1007/3-540-48157-5_29
  10. Cristiana Bolchini, Carlo A. Curino, Elisa Quintarelli, Fabio A. Schreiber, and Letizia Tanca. 2007. A data-oriented survey of context models. ACM SIGMOD Record, 36, 4 (December 2007), pp. 19-26, DOI: 10.1145/1361348.1361353
  11. C. Perera, A. Zaslavsky, P. Christen and D. Georgakopoulos, "Context Aware Computing for The Internet of Things: A Survey," in IEEE Communications Surveys & Tutorials, vol. 16, no. 1, First Quarter 2014, pp. 414-454, DOI: 10.1109/surv.2013.042313.00197
  12. Grifoni P., DUlizia A., Ferri F., Context-Awareness in Location Based Services in the Big Data Era, In: Skourletopoulos G., Mastorakis G., Mavromoustakis C., Dobre C., Pallis E. (eds) Mobile Big Data. Lecture Notes on Data Engineering and Communications Technologies, Springer, vol 10, 2018. – pp. 85–127, DOI: 10.1007/978-3- 319-67925-9_5
  13. Nicholas Capurso, Bo Mei, Tianyi Song, Xiuzhen Cheng, A survey on key fields of context awareness for mobile devices. Journal of Network and Computer Applications, Volume 118, 2018. – pp. 44-60, DOI: 10.1016/j.jnca.2018.05.006
  14. Richard S. Sutton, Andrew G. Barto, Reinforcement Learning: An Introduction, 2nd Ed., A Bradford Book, 2018. –  532 p. ISBN: 978-0-26-203924-6
  15. Csaba Szepesvari, Algorithms for Reinforcement Learning, Morgan and Claypool Publishers, 2010. – 104 p. ISBN: 978-1-60-845492-1