Multi-agent systems (MAS) are used as models for many natural and engineered systems, such as robotic teams and cell-cell interactions. Such systems exhibit time-varying spatial (spatio-temporal) behaviors.
As the complexity of MAS increases, there is a need to express their behaviors in formal ways that are interpretable to humans and amenable to rigorous mathematical analysis. In this thesis, we propose using spatio-temporal (ST) logics to write up such expressions. In addition, we address two closely related challenges 1) inferring ST logic expressions from data (the inference problem) and 2) synthesizing system inputs such that the MAS outputs meet specific behavioral requirements given by ST logic expressions (the synthesis problem). We consider two distinct MAS types 1) patterning chemical and biological systems and 2) robotic teams.
Overall, this thesis has three main parts. First, we develop ST logics that are (1) capable of describing emerging MAS behaviors and (2) equipped with qualitative and quantitative (robustness metric) semantics. The qualitative semantics address the question "are the requirements satisfied/violated?" while the quantitative semantics address the question "how well are the requirements satisfied/violated?"
Second, we develop several techniques for inferring ST logics expressions from executions of patterning systems. The proposed techniques utilize unsupervised and supervised learning techniques to learn the structure and parameters of logical expressions.
Third, we propose several methods to solve the synthesis problem when requirements are given by the ST logic formulae. We formulate the synthesis problems as optimization problems where the objective is to maximize the robustness metric, thus satisfying the requirements. We outline our approach for solving optimization problems and learning controllers using optimization and deep learning techniques.
We demonstrate the efficacy of the proposed algorithms and tools in simulated examples of patterning systems and robotic teams. We conclude with a discussion about the limitations and future research directions. / 2025-01-16T00:00:00Z
| Identifer | oai:union.ndltd.org:bu.edu/oai:open.bu.edu:2144/45454 |
| Date | 16 January 2023 |
| Creators | Alsalehi, Suhail Hasan |
| Contributors | Belta, Calin A. |
| Source Sets | Boston University |
| Language | en_US |
| Detected Language | English |
| Type | Thesis/Dissertation |
| Rights | Attribution-NonCommercial-ShareAlike 4.0 International, http://creativecommons.org/licenses/by-nc-sa/4.0/ |
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