An Abductive Interpretation for Open Agent Societies

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

doi:10.1007/978-3-540-39853-0_24

Cited by 19 publications

(24 citation statements)

References 14 publications

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“…It has been compared with (1) DPML, (2) α-algorithm, α # -algorithm, Multi-phase Macro plug-in, Genetic algorithm and Heuristics miner available in the ProM suite [49] (3) Tilde, included in the ACE Data Mining System [6], (4) two structure learning algorithms of Markov Logic Networks, the one embedded in the Alchemy package 4 and BUSL 5 .…”

Section: Methodsmentioning

confidence: 99%

“…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%

“…Models in these languages express only constraints on the process execution rather than specifying the sequence (order) of tasks to be executed as paths in a net. DecSerFlow [46], ConDec [40] and SCIFF [4,3] are examples of such languages.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

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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Section: Methodsmentioning

confidence: 99%

“…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%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Statistical relational learning for workflow mining

Bellodi

Riguzzi

Lamma

2016

IDA

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“…The language is inspired to the SCIFF one [5] and extends clausal logic by allowing more complex formulas as disjuncts in the head of clauses.…”

Section: Introductionmentioning

confidence: 99%

Applying Inductive Logic Programming to Process Mining

Lamma

Mello

Riguzzi

et al.

Inductive Logic Programming

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Abstract. The management of business processes has recently received a lot of attention. One of the most interesting problems is the description of a process model in a language that allows the checking of the compliance of a process execution (or trace) to the model. In this paper we propose a language for the representation of process models that is inspired to the SCIFF language and is an extension of clausal logic. A process model is represented in the language as a set of integrity constraints that allow conjunctive formulas as disjuncts in the head. We present an approach for inducing these models from data: we define a subsumption relation for the integrity constraints, we define a refinement operator and we adapt the algorithm ICL to the problem of learning such formulas. The system has been applied to the problem of inducing the model of a sealed bid auction and of the NetBill protocol. The data used for learning and testing were randomly generated from a correct model of the process.

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