author={Harel, David and Marron, Assaf and Wiener, Guy and Weiss, Gera},
title={Behavioral programming, decentralized control, and multiple time scales},
year={2011},
isbn={9781450311830},
publisher={Association for Computing Machinery},
address={New York, NY, USA},
url={https://doi.org/10.1145/2095050.2095079},
doi={10.1145/2095050.2095079},
abstract={Behavioral programming is a recently proposed approach for non-intrusive incremental software development. We propose that behavioral programming concepts, such as behavioral decomposition, synchronized execution of independent behaviors, and event blocking, can help in the incremental and natural coding of complex decentralized systems, complementing actor-oriented and agent-oriented approaches. We also contribute to the existing research on behavioral programming a method for coordinating behaviorally-programmed components which, due to different time scales or interaction with the external environment, cannot synchronize and thus cannot employ event blocking. We show that the resulting decentralized system retains many of the advantages present in a purely behavioral, fully synchronized system.},
booktitle={Proceedings of the Compilation of the Co-Located Workshops on DSM'11, TMC'11, AGERE! 2011, AOOPES'11, NEAT'11, \& VMIL'11},
author={Bar-Sinai, Michael and Weiss, Gera and Shmuel, Reut},
title={BPjs: an extensible, open infrastructure for behavioral programming research},
year={2018},
isbn={9781450359658},
publisher={Association for Computing Machinery},
address={New York, NY, USA},
url={https://doi.org/10.1145/3270112.3270126},
doi={10.1145/3270112.3270126},
abstract={We present unified and extensible semantics for Behavioral Programming (BP). The presented semantics support a direct embedding of executable models in regular software systems. We further present BPjs --- a tool-set for executing, embedding, and verifying behavioral models, based on the proposed semantics. Being extensible, embeddable, and supporting verification, BPjs can serve as a common infrastructure for BP and executable modeling research.},
booktitle={Proceedings of the 21st ACM/IEEE International Conference on Model Driven Engineering Languages and Systems: Companion Proceedings},
pages={59–60},
numpages={2},
keywords={executable models, javascript, scenario based models},
location={Copenhagen, Denmark},
series={MODELS '18}
}
@inproceedings{10.5555/1883978.1883995,
author={Harel, David and Marron, Assaf and Weiss, Gera},
title={Programming coordinated behavior in java},
year={2010},
isbn={3642141064},
publisher={Springer-Verlag},
address={Berlin, Heidelberg},
abstract={Following the scenario-based approach to programming which centered around live sequence charts (LSCs), we propose a general approach to software development in Java. A program will consist of modules called behavior threads (b-threads), each of which independently describes a scenario that may cross object boundaries. We identify a protocol and a coordination mechanism that allow such behavioral programming. Essentially, runs of programs are sequences of events that result from three kinds of b-thread actions: requesting that events be considered for triggering, waiting for triggered events, and blocking events requested by other b-threads. The coordination mechanism synchronizes and interlaces b-threads execution yielding composite, integrated system behavior. The protocol idioms and the coordination mechanism of b-threads are implemented as a Java library called BPJ. Throughout the exposition we illustrate benefits of the approach and discuss the merits of behavioral programming as a broad, implementation-independent paradigm.},
booktitle={Proceedings of the 24th European Conference on Object-Oriented Programming},
pages={250–274},
numpages={25},
location={Maribor, Slovenia},
series={ECOOP'10}
}
@inproceedings{10.1145/2576768.2598288,
author={Krawiec, Krzysztof and O'Reilly, Una-May},
title={Behavioral programming: a broader and more detailed take on semantic GP},
year={2014},
isbn={9781450326629},
publisher={Association for Computing Machinery},
address={New York, NY, USA},
url={https://doi.org/10.1145/2576768.2598288},
doi={10.1145/2576768.2598288},
abstract={In evolutionary computation, the fitness of a candidate solution conveys sparse feedback. Yet in many cases, candidate solutions can potentially yield more information. In genetic programming (GP), one can easily examine program behavior on particular fitness cases or at intermediate execution states. However, how to exploit it to effectively guide the search remains unclear. In this study we apply machine learning algorithms to features describing the intermediate behavior of the executed program. We then drive the standard evolutionary search with additional objectives reflecting this intermediate behavior. The machine learning functions independent of task-specific knowledge and discovers potentially useful components of solutions (subprograms), which we preserve in an archive and use as building blocks when composing new candidate solutions. In an experimental assessment on a suite of benchmarks, the proposed approach proves more capable of finding optimal and/or well-performing solutions than control methods.},
booktitle={Proceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation},
pages={935–942},
numpages={8},
keywords={archive, behavioral evaluation, genetic programming, multiobjective evolutionary computation, program semantics, program synthesis, search operators},
location={Vancouver, BC, Canada},
series={GECCO '14}
}
@inproceedings{10.1145/2038642.2038686,
author={Harel, David and Lampert, Robby and Marron, Assaf and Weiss, Gera},
title={Model-checking behavioral programs},
year={2011},
isbn={9781450307147},
publisher={Association for Computing Machinery},
address={New York, NY, USA},
url={https://doi.org/10.1145/2038642.2038686},
doi={10.1145/2038642.2038686},
abstract={System specifications are often structured as collections of scenarios and use-cases that describe desired and forbidden sequences of events. A recently proposed behavioral programming approach, which evolved from the visual language of live sequence charts (LSCs), calls for coding software modules in alignment with such scenarios. We present a methodology and a supporting model-checking tool for verifying behavioral Java programs, without having to first translate them into a specific input language for the model checker. Our method facilitates early discovery of conflicting or under-specified scenarios, which can often be resolved by adding new scenarios rather than by changing existing code. Also, counterexamples provided by the tool are themselves event sequences that can serve directly for refinements and corrections. Our tool reduces the size of the execution state-space using an abstraction that focuses on behaviorally interesting states and treats transitions between them as atomic.},
booktitle={Proceedings of the Ninth ACM International Conference on Embedded Software},
pages={279–288},
numpages={10},
keywords={behavioral programming, java},
location={Taipei, Taiwan},
series={EMSOFT '11}
}
@inproceedings{10.1109/MODELS-C.2019.00039,
author={Elyasaf, Achiya and Sadon, Aviran and Weiss, Gera and Yaacov, Tom},
title={Using behavioural programming with solver, context, and deep reinforcement learning for playing a simplified RoboCup-type game},
abstract={We describe four scenario-based implementations of controllers for a player in a simplified RoboCup-type game. All four implementations are based on the behavioural programming (BP) approach. We first describe a simple controller for the player using the state-of-the-art BPjs tool and then show how it can be extended in various ways. The first extension is based on a version of BP where the Z3 SMT solver is used to provide mechanisms for richer composition of modules within the BP model. This allows for modules with higher cohesion and lower coupling. It also allows incrementality: we could use the scenarios we developed for the challenge of MDETOOLS'18 and extend the model to handle the new system. The second extension of BP demonstrated in this paper is a set of idioms for subjecting model components to context. One of the differences between this year's challenge and the challenge we dealt with last year is that following the ball is not the only task that a player needs to handle, there is much more to care for. We demonstrate how we used the idioms for handling context to parametrize scenarios like "go to a target" in a dynamic and natural fashion such that modelers can efficiently specify reusable components similar to the way modern user manuals for advanced products are written. Lastly, in an attempt to make the instructions to the robot even more natural, we demonstrate a third extension based on deep reinforcement learning. Towards substantiating the observation that it is easier to explain things to an intelligent agent than to dumb compiler, we demonstrate how the combination of BP and deep reinforcement learning (DRL) allows for giving abstract instructions to the robot and for teaching it to follow them after a short training session.},
booktitle={Proceedings of the 22nd International Conference on Model Driven Engineering Languages and Systems},
