Abstract
Internet of Things (IoT) allows for cyber-physical applications to be created and composed to provide intelligent support or automation of end-user tasks. For many of such tasks, human participation is crucial to the success and the quality of the tasks. The cyber systems should proactively request help from the humans to accomplish the tasks when needed. However, the outcome of such system-human synergy may be affected by factors external to the systems. Failure to consider those factors when involving human participants in the tasks may result in suboptimal performance and negative experience on the humans. In this paper, we propose an approach for automated generation of control strategies of cyber-human systems. We investigate how explicit modeling of human participant can be used in automated planning to generate cooperative strategy of human and system to achieve a given task, by means of which best and appropriately utilize the human. Specifically, our approach consists of: (1) a formal framework for modeling cooperation between cyber system and human, and (2) a formalization of system-human cooperative task planning as strategy synthesis of stochastic multiplayer game. We illustrate our approach through an example of indoor air quality control in smart homes.
This work is supported by Bosch Research and Technology Center North America.
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Notes
- 1.
We illustrate our approach to modeling the SMG using the syntax of the PRISM language [3] for SMG, which are encoded as commands:
$$\begin{aligned}{}[action] \, guard \, -> \, p_1 : u_1+ ... + p_n : u_n \end{aligned}$$where guard is a predicate over the model variables. Each update \(u_i\) describes a transition that the process can make (by executing action) if the guard is true. An update is specified by giving the new values of the variables, and has an assigned probability \(p_i \in [0, 1]\). Multiple commands with overlapping guards (and probably, including a single update of unspecified probability) introduce local nondeterminism.
- 2.
In this example, player env only has deterministic behavior. However, in general, it can have probabilistic and nondeterministic behavior as well.
- 3.
We do not generate strategies for a coalition of players sys and hum because, in addition to the cooperative behavior between the human and the system, we also want the planning model to capture the human behavior that is independent of the system. Such behavior can also affect how the task must be performed.
- 4.
To simplify the analysis, we use a single value of \(p_W\) for both when the occupant is and is not busy.
References
Chen, T., et al.: Automatic verification of competitive stochastic systems. Formal Methods Syst. Des. 43(1), 61–92 (2013)
Kwiatkowska, M., Parker, D.: Automated verification and strategy synthesis for probabilistic systems. In: Van Hung, D., Ogawa, M. (eds.) ATVA 2013. LNCS, vol. 8172, pp. 5–22. Springer, Heidelberg (2013)
Kwiatkowska, M., Norman, G., Parker, D.: PRISM 4.0: Verification of probabilistic real-time systems. In: Computer Aided Verification. Springer, Berlin (2011)
Chen, T., Forejt, V., Kwiatkowska, M., Parker, D., Simaitis, A.: PRISM-games: a model checker for stochastic multi-player games. In: Piterman, N., Smolka, S.A. (eds.) TACAS 2013 (ETAPS 2013). LNCS, vol. 7795, pp. 185–191. Springer, Heidelberg (2013)
Eskins, D., Sanders, W.H.: The multiple-asymmetric-utility system model: a framework for modeling cyber-human systems. In: 2011 Eighth International Conference on Quantitative Evaluation of Systems (QEST). IEEE (2011)
Cámara, J., Moreno, G.A., Garlan, D.: Reasoning about human participation in self-adaptive systems. In: Proceedings of the 10th International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS 2015) (2015)
Fu, J., Topcu, U.: Pareto efficiency in synthesizing shared autonomy policies with temporal logic constraints (2014). arXiv preprint arXiv:1412.6029
Li, W., Sadigh, D., Sastry, S.S., Seshia, S.A.: Synthesis for human-in-the-loop control systems. In: Ábrahám, E., Havelund, K. (eds.) TACAS 2014 (ETAPS). LNCS, vol. 8413, pp. 470–484. Springer, Heidelberg (2014)
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Sukkerd, R., Garlan, D., Simmons, R. (2015). Task Planning of Cyber-Human Systems. In: Calinescu, R., Rumpe, B. (eds) Software Engineering and Formal Methods. SEFM 2015. Lecture Notes in Computer Science(), vol 9276. Springer, Cham. https://doi.org/10.1007/978-3-319-22969-0_21
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DOI: https://doi.org/10.1007/978-3-319-22969-0_21
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