Clouseau: Generating Communication Protocols from Commitments

Singh, Munindar P.; Chopra, Amit K.

doi:10.1609/aaai.v34i05.6215

Cited by 8 publications

(5 citation statements)

References 29 publications

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“…They observe that BSPL is "more of a core calculus than a usable notation." Recent work on meaning-based languages that compile into BSPL appears to supports the idea that BSPL represents a core calculus (Singh & Chopra, 2020). Winikoff et al also report an extensive user study that evaluated HAPN, AUML, and statecharts for ease of reading, understanding, and writing specifications.…”

Section: Discussionmentioning

confidence: 97%

An Evaluation of Communication Protocol Languages for Engineering Multiagent Systems

Chopra

Christie²,

Singh³

2020

jair

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Communication protocols are central to engineering decentralized multiagent systems. Modern protocol languages are typically formal and address aspects of decentralization, such as asynchrony. However, modern languages differ in important ways in their basic abstractions and operational assumptions. This diversity makes a comparative evaluation of protocol languages a challenging task. We contribute a rich evaluation of diverse and modern protocol languages. Among the selected languages, Scribble is based on session types; Trace-C and Trace-F on trace expressions; HAPN on hierarchical state machines, and BSPL on information causality. Our contribution is four-fold. One, we contribute important criteria for evaluating protocol languages. Two, for each criterion, we compare the languages on the basis of whether they are able to specify elementary protocols that go to the heart of the criterion. Three, for each language, we map our findings to a canonical architecture style for multiagent systems, highlighting where the languages depart from the architecture. Four, we identify design principles for protocol languages as guidance for future research.

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Section: Discussionmentioning

confidence: 97%

An Evaluation of Communication Protocol Languages for Engineering Multiagent Systems

Chopra

Christie²,

Singh³

2020

jair

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“…Each commitment protocol is a sequence of actions, such as create, select, cancel, release, detach, satisfy, discharge, expire, violate, assign, and delegate, that pursue a well-defined life-cycle, including states such as violated, satisfied, and expired [30]. The formal definition of commitment antecedents and consequences as logic propositions as well as the predefined sequence of actions of each protocol supports protocol synthesis [43] and proving properties about agent teams [42]. As compared to message-based agent interactions [44], which prescribe an automaton-like sequence of steps, commitment protocols are more flexible in describing the nature and parameters of an agent's interactions [30].…”

Section: Related Workmentioning

confidence: 99%

Studying Consensus and Disagreement during Problem Solving in Teams through Learning and Response Generation Agents Model

Doboli

Curiac

2023

Mathematics

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Understanding the process of reaching consensus or disagreement between the members of a team is critical in many situations. Consensus and disagreement can refer to various aspects, such as requirements that are collectively perceived to be important, shared goals, and solutions that are jointly considered to be realistic and effective. Getting insight on how the end result of the interaction process is influenced by parameters such as the similarity of the participants’ experience and behavior (e.g., their available concepts, the produced responses and their utility, the preferred response generation method, and so on) is important for optimizing team performance and for devising novel applications, i.e., systems for tutoring or self-improvement and smart human computer interfaces. However, understanding the process of reaching consensus or disagreement in teams raises a number of challenges as participants interact with each other through verbal communications that express new ideas created based on their experience, goals, and input from other participants. Social and emotional cues during interaction are important too. This paper presents a new model, called Learning and Response Generating Agents, for studying the interaction process during problem solving in small teams. As compared to similar work, the model, grounded in work in psychology and sociology, studies consensus and disagreement formation when agents interact with each other through symbolic, dynamically-produced responses with clauses of different types, ambiguity, multiple abstraction levels, and associated emotional intensity and utility.

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“…Tosca [17] explores the layering of commitments on top of information protocols from the point of view of alignment. Clouseau [18] aims at a protocol language that unifies the operational and higher-level aspects in a single language.…”

Section: Operational Protocolsmentioning

confidence: 99%

Interaction-Oriented Software Engineering: Programming abstractions for autonomy and decentralization

Chopra

2022

AIC

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We review the main ideas and elements of Interaction-Oriented Software Engineering (IOSE), a program of research that we have pursued for the last two decades, a span of time in which it has grown from philosophy to practical programming abstractions. What distinguishes IOSE from any other program of research is its emphasis on supporting autonomy by modeling the meaning of communication and using that as the basis for engineering decentralized sociotechnical systems. Meaning sounds esoteric but is the basis for practical decision making and a holy grail for the field of distributed systems. We describe our contributions so far, directions for research, and the potential for broad impact on computing.

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Clouseau: Generating Communication Protocols from Commitments

Cited by 8 publications

References 29 publications

An Evaluation of Communication Protocol Languages for Engineering Multiagent Systems

An Evaluation of Communication Protocol Languages for Engineering Multiagent Systems

Studying Consensus and Disagreement during Problem Solving in Teams through Learning and Response Generation Agents Model

Interaction-Oriented Software Engineering: Programming abstractions for autonomy and decentralization

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