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StreamB: A Declarative Language for Automatically Processing Data Streams in Abstract Environments for Agent Platforms

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Engineering Multi-Agent Systems (EMAS 2021)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13190))

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Abstract

To apply BDI agents to real-world scenarios, the reality-gap, between the low-level data (perceptions) and their high-level representation (beliefs), must be bridged. This is usually achieved by a manual mapping. There are two problems with this solution: (i) if the environment changes, the mapping has to be changed as well (by the developer); (ii) part of the mapping might end up being implemented at the agent level increasing the code complexity and reducing its generality. In this paper, we present a general approach to automate the mapping between low-level data and high-level beliefs through the use of transducers. These transducers gather information from the environment and map them to high-level beliefs according to formal temporal specifications. We present our technique and we show its applicability through a case study involving the remote inspection of a nuclear plant.

Work supported by UK Research and Innovation, and EPSRC Hubs for “Robotics and AI in Hazardous Environments”: EP/R026173 (ORCA), and EP/R026084 (RAIN).

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Notes

  1. 1.

    It computes the standard deviation over the data stream.

  2. 2.

    It is important to note that instead of a number we may have a stream.

  3. 3.

    https://www.antlr.org/.

  4. 4.

    https://github.com/autonomy-and-verification-uol/StreamB.

  5. 5.

    https://www.akka-technologies.com/.

  6. 6.

    https://github.com/mcapl/mcapl.

  7. 7.

    http://wiki.ros.org/rosbridge.

  8. 8.

    https://clearpathrobotics.com/jackal-small-unmanned-ground-vehicle/.

  9. 9.

    Or partially, in case we extend an existing abstract environment as we did for MCAPL.

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Author information

Authors and Affiliations

  1. Department of Computer Science, Bioengineering, Robotics and Systems Engineering (DIBRIS), University of Genoa, Genoa, Italy

    Angelo Ferrando

  2. Department of Computer Science, University of Liverpool, Liverpool, UK

    Fabio Papacchini

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  1. Angelo Ferrando
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  2. Fabio Papacchini
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Correspondence to Angelo Ferrando or Fabio Papacchini .

Editor information

Editors and Affiliations

  1. Utrecht University, Utrecht, The Netherlands

    Natasha Alechina

  2. University of Turin, Turin, Italy

    Matteo Baldoni

  3. Utrecht University, Utrecht, The Netherlands

    Brian Logan

A How Reviewers’ Comments Have Been Addressed

A How Reviewers’ Comments Have Been Addressed

1.1 A.1 Review 1

Comment: The hypothesis of a global clock is unsuitable in real-world applications. Modern data streaming techniques (see for example AkkaStream) overcome this limitation by implementing standard protocols (for instance see “back-pressure”).

Answer: We added more explanations on this aspect (see Sect. 4). Briefly, it is true that Akka would simplify the transducers’ implementation, but the global clock generated by the clock node in StreamB does not only guarantee the same pace over the events, but allows synchronising the events, so that the resulting discretised sequence can be uniformly quantified by the MTL properties. In any case, such a global clock is enforced by StreamB, not assumed.

1.2 A.2 Review 2

Comment: One of the main arguments for StreamB used throughout the paper is to avoid “manual mapping” (or “manual coding”) from low-level data streams to beliefs, e.g. in the abstract: “if the environment changes, the mapping has to be changed as well (by the developer)”. StreamB is a high-level DSL that allows processing data streams using concise scripts/programs, which is great, but the mapping is still manual. Transducers and pipelines are synthesized based on StreamB programs, but this only hides away the complexity, I don’t see how it removes the need to change the mapping if the environment changes. This point should be better clarified.

Answer: The reviewer is right. We fixed this aspect and updated the text in Sect. 1 accordingly. Indeed, the mapping is still manual, but achieved through a higher-level specification (the DSL), which hopefully simplifies and improve the engineering process.

Comment: It is a bit surprising that the environment abstraction creates beliefs for agents, this blurs the separation of concerns between agents and the environment abstraction. Also, why limit this contribution to BDI agents? The impact could be broader. To illustrate the point with an example, in CArtAgO (discussed in related work) this problem is solved by introducing a separation of concerns between the agent mind and the agent body. The agent body keeps track of stimuli from the environment, but how the stimuli are interpreted and used is the concern of the agent mind and depends on the agent model (BDI or other).

Answer: We added more explanations on this point in Sect. 7. It is true that the work could be more general, and considering its implementation, it is actually the case. Since no restriction is given on what the abstract environment should do with the events generated by the fourth layer of transducers. Nonetheless, from a presentation perspective, we preferred to keep it focused on BDI systems, since we believe is the area in MAS which would gain more from our approach.

Comment: It could be misleading to say StreamB is a DSL for synthesizing abstract environments. In my understanding, it is a solution to a more specific problem: processing data streams in abstract environments. The authors also point this out and open the contribution to integration with existing MAS frameworks as part of the environment dimension.

Answer: The reviewer is right. We updated the title and Sect. 1 accordingly. StreamB does not completely synthesise an abstract environment, but it automatically synthesises transducers that can be used to develop an abstract environment. The development of the abstract environment though would be extremely straightforward, since it would only need to subscribe to the corresponding high-level events.

Comment: It is not clear to me why if the environment abstraction is designed and programmed manually, then it is addressed in an ad-hoc manner: on the contrary, it becomes a central part of the design of the overall system. Not a criticism and not intended to minimize the contribution, but note that with StreamB the design of the processing pipelines is also manual, only at a higher level of abstraction: the underlying machinery just executes what is written in StreamB programs.

Answer: The reviewer is right, we updated Sect. 1 accordingly.

Comment: As a suggestion for future work, the StreamB processing pipelines are currently hidden to agents (i.e., agents are not aware of the processing pipelines), but if the pipelines themselves would become first-class abstractions in the environment (i.e., pipelines that agents are aware of and can manipulate).

Answer: The reviewer pointed out very interesting future directions. We integrated these suggestions in Sect. 7.

Comment: The paper could use another thorough reading.

Answer: We fixed all the reported typos.

1.3 A.3 Review 3

Comment: The literature (e.g., in the field of functional programming) is rich of proposals for stream processing languages, including DSLs (e.g., various papers in the PADL conference). It would be interesting to see a comparison between StreamB and these other approaches and some discussion of why StreamB was built independently instead of using one of such existing frameworkds.

Answer: We extended Sect. 6 to consider existing Stream Processing Languages. We reported some of them, and compared with StreamB.

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Ferrando, A., Papacchini, F. (2022). StreamB: A Declarative Language for Automatically Processing Data Streams in Abstract Environments for Agent Platforms. In: Alechina, N., Baldoni, M., Logan, B. (eds) Engineering Multi-Agent Systems. EMAS 2021. Lecture Notes in Computer Science(), vol 13190. Springer, Cham. https://doi.org/10.1007/978-3-030-97457-2_7

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