Verifiable agent interaction in abductive logic programming

Alberti, Marco; Chesani, Federico; Gavanelli, Marco; Lamma, Evelina; Mello, Paola; Torroni, Paolo

doi:10.1145/1380572.1380578

Cited by 119 publications

(157 citation statements)

References 59 publications

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“…Expectations have also been used to formalize business protocols as described in the SCIFF framework [9]. SCIFF is based on abductive logic, and it does not only specify a business protocol, but also helps verify agent interactions in open systems.…”

Section: Discussionmentioning

confidence: 99%

Detecting Exceptions in Commitment Protocols: Discovering Hidden States

Kafalı

Yolum

2010

Languages, Methodologies, and Development Tools for Multi-Agent Systems

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Abstract-Open multiagent systems consist of autonomous agents that are built by different vendors. In principle, open multiagent systems cannot provide any guarantees about the behaviors of their agents. This means that when agents are working together, such as carrying out a business protocol, one agent's misbehavior may potentially create an exception for another agent and obstruct its proper working. Faced with such an exception, an agent should be able to identify the problem by verifying the compliance of other agents.Previous work on verification of protocols unrealistically assume that participants have full knowledge of a protocol. However, when multiple agents enact a protocol, each agent has access to its part of the protocol and not more. This will require agents to check verification by querying others and more importantly by discovering the contracts between them. Here, we propose a commitment-based framework for detecting exceptions in which an agent augments its part of the protocol with its knowledge to construct states that are previously hidden to the agent by generating possible commitments between other agents. The agent then queries others to confirm those states. Our framework is formalized using C+ and is tested using a realistic business scenario.

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Section: Discussionmentioning

confidence: 99%

Detecting Exceptions in Commitment Protocols: Discovering Hidden States

Kafalı

Yolum

2010

Languages, Methodologies, and Development Tools for Multi-Agent Systems

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“…The process language we consider is a subset of the declarative SCIFF language, originally defined in [3,4] for specifying and verifying interaction in open agent societies.…”

Section: Process Modelsmentioning

confidence: 99%

“…SCIFF [4,3] is a declarative language based, instead, on computational logic, which models processes with Social Integrity Constraints as forward rules of the form Body → Head, where Body can contain literals and happened events, and Head contains a disjunction of conjunctions of expectations of events and literals. ConDec/DecSerFlow can be translated into SCIFF and a subset of SCIFF can be translated into ConDec/DecSerFlow [10].…”

Section: Related Workmentioning

confidence: 99%

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Statistical relational learning for workflow mining

Bellodi

Riguzzi

Lamma

2016

IDA

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The management of business processes can support efficiency improvements in organizations. One of the most interesting problems is the mining and representation of process models in a declarative language. Various recently proposed knowledge-based languages showed advantages over graph-based procedural notations. Moreover, rapid changes of the environment require organizations to check how compliant are new process instances with the deployed models. We present a Statistical Relational Learning approach to Workflow Mining that takes into account both flexibility and uncertainty in real environments. It performs automatic discovery of process models expressed in a probabilistic logic. It uses the existing DPML algorithm for extracting first-order logic constraints from process logs. The constraints are then translated into Markov Logic to learn their weights. Inference on the resulting Markov Logic model allows a probabilistic classification of test traces, by assigning them the probability of being compliant to the model. We applied this approach to three datasets and compared it with DPML alone, five Petri net-and EPC-based process mining algorithms and Tilde. The technique is able to better classify new execution traces, showing higher accuracy and areas under the PR/ROC curves in most cases.

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“…Here, in fact, it is necessary to coordinate autonomous and heterogeneous agents and it is not possible to assume mutual trust among them. It is necessary to have an unambiguous semantics allowing the verification of interaction properties both before the interaction takes place [35] as well as during the interaction [1], preserving at the same time the privacy of the implemented policies. Figure 1 draws the overall picture of the proposal.…”

Section: The Mercurio Frameworkmentioning

confidence: 99%

An Interaction-Oriented Agent Framework for Open Environments

Baldoni

Baroglio

Bergenti

et al. 2011

Lecture Notes in Computer Science

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Abstract. The aim of the work is to develop formal models of interaction and of the related support infrastructures, that overcome the limits of the current approaches. We propose to represent explicitly not only the agents but also the computational environment in terms of rules, conventions, resources, tools, and services, that are functional to the coordination and cooperation of the agents. These models will enable the verification of the interaction in the MAS, thanks to the introduction of a novel social semantics of interaction based on commitments and on an explicit account of the regulative rules.

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Verifiable agent interaction in abductive logic programming

Cited by 119 publications

References 59 publications

Detecting Exceptions in Commitment Protocols: Discovering Hidden States

Detecting Exceptions in Commitment Protocols: Discovering Hidden States

Statistical relational learning for workflow mining

An Interaction-Oriented Agent Framework for Open Environments

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