Ioan Alfred Letia

Diagnosis of the Dynamics within an Organization by Trace Checking of Behavioural Requirements

The main question addressed in this paper is how requirements on the dynamics within an organization model can be specified and how the dynamics within such an organization can be formally analysed. A specification language is proposed, and a number of different types of requirements for dynamics at different levels in the organization are identified. Based on a logical analysis and a software environment to check requirements against traces of the dynamics, a diagnostic method is proposed to analyse the malfunctioning of an organization, and pinpoint causes of malfunctioning.

Cloud motion detection from infrared satellite images

The estimation of cloud motion from a sequence of satellite images can be considered a challenging task due to the complexity of phenomena implied. Being a non-rigid motion and implying non-linear events, most motion models are not suitable and new algorithms have to be developed. We propose a novel technique, combining a Block Matching Algorithm (BMA) and a best candidate block search along with a vector median regularisation.

Compliance checking of integrated business processes

To enable compliance checking on integrated business processes we developed the NTL-ALC logical framework, for closing the gap between the abstract norms and the concrete business processes. To reason on the active obligations and permissions, we extended the normative temporal logic NTL, by applying the deontic operators O (obligation) and P (permission) on concepts of the ALC (Attribute Language with Complements) description logic. As proof of concept of our results we have used the Hazard Analysis at Critical Control Points (HACCP) standard, aiming to prevent the occurrence of significant hazards in the food industry.

Arguing with justifications between collaborating agents

We exploit the Justification Logic capabilities of reasoning about justifications, comparing pieces of evidence, and measuring the complexity of justifications in the context of argumentative agents. The research can be integrated into the larger context of integrating logic and argumentation. The paper introduces distributed justification logic

Self-organizing Maps in Web Mining and Semantic Web

\mathcal{DJL}

Defeasible Protocols in Persuasion Dialogues

as an extension of justification logic for multi-agent systems, and it also investigates the expressivity of

Running contracts with defeasible commitment

\mathcal{DJL}

Plausible Description Logic Programs for Stream Reasoning

for argumentative agents. Not knowing all of the implications of their knowledge base, agents use justified arguments for reflection and guidance.

Automating the dispute resolution in a task dependency network

The nature inspired approaches represent a new trend in computer science in general and in the Semantic Web, due to their scalability and robustness. Neural networks represent one category of nature inspired solutions. The self-organizing map (SOM) is a very popular unsupervised neural network model (Kohonen, et al., 2000). It is a data mining and visualization method for complex high dimensional data sets. In the first part of the chapter, we present how the SOM model can be applied in Web mining, by giving sets of documents as input data space for SOM. The result of applying SOM on a set of documents is a map of documents, which is organized in a meaningful manner so that documents with similar content appear at nearby locations on the twodimensional map display. From the information retrieval point of view, our implemented SOM-based system creates document maps that are readily organized for browsing. A document map also clusters the data, resulting in an approximate model of the data distribution in the high dimensional document space. Some experimental results are included, where a couple of meaningful clusters have been discovered by our system in a subset of the “20 newsgroups” data set (Lang, K., 1995). The clustering capability of our system allows users to find out quickly what is new in a Web site of interest by comparing the clusters obtained from the site at different moments in time. In the rest of the chapter, we focus on how a more complex SOM based unsupervised neural network model is used for enriching a domain ontology. Building complete and reliable domain ontologies is the basis for the success of the Semantic Web. The ontology enrichment process consists in the addition of new concepts which will be attached as hyponyms for the existent nodes of the ontology (Pekar and Staab, 2002). The names of the new concepts are terms represented linguistically by common noun phrases. The enrichment process can also add new instances to existent concepts of the ontology. In this case, the process is also known in the literature as ontology population or named entity classification, where the named entities are represented linguistically by proper names of people, organizations, locations etc. (Cimiano and Volker, 2005). In both cases, the process is algorithmically the same, the only difference being the grammatical category of the linguistic entities to be classified: common noun phrases representing terms for new concepts to be added or proper noun phrases representing named entities, i.e. new instances for the existent 22

Assuring safety in air traffic control systems with argumentation and model checking

Defeasible protocols lead to a more refined and accurate handling of persuasion dialogues, important in human-artificial agent communication, a step forward to effective practical argumentation support systems