Abstract
Monitoring plan execution is useful in various multi-agent applications, from agent cooperation to norm enforcement. Realistic environments often impose constraints on the capabilities of such monitoring, limiting the amount and coverage of available sensors. In this paper, we consider the problem of sensor placement within an environment to determine whether some behaviour has occurred. Our model is based on the semantics of planning, and we provide a simple formalism for describing sensors and behaviours in such a model. Given the computational complexity of the sensor placement problem, we investigate heuristic techniques for performing sensor placement, demonstrating that such techniques perform well even in complex domains.
R. Fraga Pereira—This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nivel Superior – Brasil (CAPES).
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- 1.
Fluents are ground logical predicates, which can either be positive or negated, and include constants for truth (\(\top \)) and falsehood (\(\bot \)).
- 2.
A state formula is comprised of a finite set fluents that represent logical values according to some interpretation.
- 3.
F1-Score is the harmonic mean between Precision (i.e., positive predictive value) and Recall (i.e., true positive rate).
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Meneguzzi, F., Fraga Pereira, R., Oren, N. (2018). Sensor Placement for Plan Monitoring Using Genetic Programming. In: Miller, T., Oren, N., Sakurai, Y., Noda, I., Savarimuthu, B.T.R., Cao Son, T. (eds) PRIMA 2018: Principles and Practice of Multi-Agent Systems. PRIMA 2018. Lecture Notes in Computer Science(), vol 11224. Springer, Cham. https://doi.org/10.1007/978-3-030-03098-8_40
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DOI: https://doi.org/10.1007/978-3-030-03098-8_40
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