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Authors: Tom Yaacov 1 ; Gera Weiss 1 ; Adiel Ashrov 2 ; Guy Katz 2 and Jules Zisser 1

Affiliations: 1 Ben-Gurion University of the Negev, Israel ; 2 The Hebrew University of Jerusalem, Israel

Keyword(s): Behavioral Programming, Deep Reinforcement Learning, Formal Methods.

Abstract: We explore and evaluate the interactions between Behavioral Programming (BP) and a range of Artificial Intelligence (AI) and Formal Methods (FM) techniques. Our goal is to demonstrate that BP can serve as an abstraction that integrates various techniques, enabling a multifaceted analysis and a rich development process. Specifically, the paper examines how the BPpy framework, a Python-based implementation of BP, is enhanced by and enhances various FM and AI tools. We assess how integrating BP with tools such as Satisfiability Modulo Theory (SMT) solvers, symbolic and probabilistic model checking, and Deep Reinforcement Learning (DRL) allow us to scale the abilities of BP to model complex systems. Additionally, we illustrate how developers can leverage multiple tools within a single modeling and development task. The paper provides quantitative and qualitative evidence supporting the feasibility of our vision to create a comprehensive toolbox for harnessing AI and FM methods in a unifi ed development framework. (More)

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Paper citation in several formats:
Yaacov, T., Weiss, G., Ashrov, A., Katz, G. and Zisser, J. (2025). Exploring and Evaluating Interplays of BPpy with Deep Reinforcement Learning and Formal Methods. In Proceedings of the 20th International Conference on Evaluation of Novel Approaches to Software Engineering - ENASE; ISBN 978-989-758-742-9; ISSN 2184-4895, SciTePress, pages 27-40. DOI: 10.5220/0013215200003928

@conference{enase25,
author={Tom Yaacov and Gera Weiss and Adiel Ashrov and Guy Katz and Jules Zisser},
title={Exploring and Evaluating Interplays of BPpy with Deep Reinforcement Learning and Formal Methods},
booktitle={Proceedings of the 20th International Conference on Evaluation of Novel Approaches to Software Engineering - ENASE},
year={2025},
pages={27-40},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013215200003928},
isbn={978-989-758-742-9},
issn={2184-4895},
}

TY - CONF

JO - Proceedings of the 20th International Conference on Evaluation of Novel Approaches to Software Engineering - ENASE
TI - Exploring and Evaluating Interplays of BPpy with Deep Reinforcement Learning and Formal Methods
SN - 978-989-758-742-9
IS - 2184-4895
AU - Yaacov, T.
AU - Weiss, G.
AU - Ashrov, A.
AU - Katz, G.
AU - Zisser, J.
PY - 2025
SP - 27
EP - 40
DO - 10.5220/0013215200003928
PB - SciTePress

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