SCIFF is a framework thought to specify and verify interaction in open agent societies. The SCIFF language is equipped with a semantics based on abductive logic programming; SCIFF's operational component is a new abductive logic programming proof procedure, also named SCIFF, for reasoning with expectations in dynamic environments. In this article we present the declarative and operational semantics of the SCIFF language, and the termination, soundness, and completeness results of the SCIFF proof procedure, and we demonstrate SCIFF's possible application in the multiagent domain. (http://lia.deis.unibo.it/research/ socs/), and by the MIUR PRIN 2005 projects 2005-011293 (Specifica e verifica di protocolli di interazione fra agenti) and 2005-015491 (Vincoli e preferenze come formalismo unificante per l'analisi di sistemi informatici e la soluzione di problemi reali).
In the last few years, there has been a growing interest in the adoption of declarative paradigms for modeling and verifying process models. These paradigms provide an abstract and human understandable way of specifying constraints that must hold among activities executions rather than focusing on a specific procedural solution. Mining such declarative descriptions is still an open challenge. In this paper, we present a logic-based approach for tackling this problem. It relies on Inductive Logic Programming techniques and, in particular, on a modified version of the Inductive Constraint Logic algorithm. We investigate how, by properly tuning the learning algorithm, the approach can be adopted to mine models expressed in the ConDec notation, a graphical language for the declarative specification of business processes. Then, we sketch how such a mining framework has been concretely implemented as a ProM plug-in called DecMiner. We finally discuss the effectiveness of the approach by means of an example which shows the ability of the language to model concurrent activities and of DecMiner to learn such a model.
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.
In multiagent systems, agent interaction is ruled by means of interaction protocols. Compliance to protocols can be hardwired in agent programs; however, this requires that only ''certified'' agents interact. In open societies, composed of autonomous and heterogeneous agents whose internal structure is, in general, not accessible, interaction protocols should be specified in terms of the agent observable behaviour, and compliance should be verified by an external entity. In this paper, we propose a Java-Prolog-CHR system for verification of compliance of agents' behaviour to protocols specified in a logic-based formalism (Social Integrity Constraints). We also present the application of the formalism and the system to the specification and verification of the FIPA Contract-Net protocol
Representing uncertain information is crucial for modeling real world domains. In this paper we present a technique for the integration of probabilistic information in Description Logics (DLs) that is based on the distribution semantics for probabilistic logic programs. In the resulting approach, that we called DISPONTE, the axioms of a probabilistic knowledge base (KB) can be annotated with a real number between 0 and 1. A probabilistic knowledge base then defines a probability distribution over regular KBs called worlds and the probability of a given query can be obtained from the joint distribution of the worlds and the query by marginalization. We present the algorithm BUNDLE for computing the probability of queries from DISPONTE knowledge bases. The algorithm exploits an underlying DL reasoner, such as Pellet, that is able to return explanations for queries. The explanations are encoded in a Binary Decision Diagram from which the probability of the query is computed. The experimentation of BUNDLE on probabilistic knowledge bases shows that it can handle knowledge bases of realistic size.
In this work we propose an approach for the automatic discovery of logic-based models starting from a set of process execution traces. The approach is based on a modified Inductive Logic Programming algorithm, capable of learning a set of declarative rules. \ud The advantage of using a declarative description is twofold. First, the process is represented in an intuitive and easily readable way; second, a family of proof procedures associated to the chosen language can be used to support the monitoring and management of processes (conformance testing, properties verification and interoperability checking, in particular).\ud The approach consists in first learning integrity constraints expressed as logical formulas and then translating them into a declarative graphical language named DecSerFlow.\ud We demonstrate the viability of the approach by applying it to a real dataset from a health case process and to an artificial dataset from an e-commerce protocol
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