International Journal on Artificial Intelligence Tools

In a future vision of Ambient Intelligence — or AmI — our surrounding environment will integrate a pervasive, interconnected network of sensors, intelligent appliances and computer-like devices. This implies, on the one hand, hardware and interface related issues, and, on the other hand, a layer of context-aware services that manages the large quantities of information generated throughout a system formed mostly of devices with limited capabilities. This paper presents the first steps toward the realization of the AmIciTy framework: a multi-agent system that relies on local interaction and the self-organization of agents, having as purpose the context-aware sharing of pieces of information. The paper presents the structure of the system, the design of the agents, the manner of building scenarios, experiments and the evaluation of a prototype.

The interactions between parties within large open systems are driven by trust. A major component in building trust is represented by reputation. When the trust value of a target agent, computed by its partner based on previous experience, and the reputation it has been assigned are different, an agreement is reached by negotiation. This paper describes a normative multiagent system and proposes a negotiation model where reputation represents the leverage that ensures norm enforcement. The negotiation framework is comprised of a marketplace using barter exchanges. Based on this model, the paper shows how self-interested agents manage to establish cooperation relationships in order to accomplishing their goals, being aware of the influence that reputation has on the costs of future transactions.

Business process management is a challenging task that requires business processes being described, executed, monitored and continuously enhanced. This process management lifecycle requires business as well as IT people working together, whereby the view on business process is quite different on both sides. One important means for bridging the gap between both consists in having a modeling notation that can be easily understood but also has a precise semantics and can be used as a basis for workflow execution. Although existing approaches like BPMN and EPCs aim at being such as notation they are already very activity oriented and do not consider the underlying motivations of processes. Introducing the goal oriented process modeling notation (GPMN) a new language is presented that has the objective of bringing together both sides by establishing higher-level modeling concepts for workflows. This results in an increased intelligibility of workflow descriptions for business people and greater consideration for the way processes are described on the business side. The core idea of the approach consists in introducing different kinds of goals and goal relationships in addition to the established activity-centered behavior model. The applicability of the approach is further illustrated with an example workflow from Daimler AG.

Rule Responder is a Pragmatic Web infrastructure for distributed rule-based event processing multi-agent eco-systems. This allows specifying virtual organizations — with their shared and individual (semantic and pragmatic) contexts, decisions, and actions/events for rule-based collaboration between the distributed members. The (semi-)autonomous agents use rule engines and Semantic Web rules to describe and execute derivation and reaction logic which declaratively implements the organizational semiotics and the different distributed system/agent topologies with their negotiation/coordination mechanisms. They employ ontologies in their knowledge bases to represent semantic domain vocabularies, normative pragmatics and pragmatic context of event-based conversations and actions.

This paper introduces a new collaborative approach to construction of large scale service oriented systems supporting distributed reasoning. In particular, we assume systems in which complex situation assessment is carried out through composition of heterogeneous services, each specialized for a particular type of analysis. Different services are composed automatically by using service discovery and negotiation. One of the major challenges in such settings is efficient definition of a large number of different types of services. The presented solution supports efficient definition of services by using a combination of light weight service ontologies, efficient construction procedures and tools. In particular, machine-understandable descriptions of heterogeneous services with well defined syntax and semantics can be created by multiple designers, without complex coordination of collaborative design processes and without any knowledge of formal ontologies.

The paper discusses a new knowledge representation for rule-based systems called XTT2. It combines decision trees and decision tables forming a transparent and hierarchical visual representation of the decision units linked into a workflow-like structure. There are two levels of abstraction in the XTT2 model: the lower level, where a single knowledge component defined by a set of rules working in the same context is represented by a single decision table, and the higher level, where the structure of the whole knowledge base is considered. This model has a concise formalization which opens up possibility of well-defined, structured design and verification of formal characteristics. Based on the visual XTT2 model, a textual representation of the rule base is generated. A dedicated engine provides a unified run-time environment for the XTT2 rule bases. The focus of the paper is on the formalization of the presented approach. It is based on the so-called ALSV(FD) logic that provides an expressive calculus for rules.

Ontology instance migration is one of the complex and not fully solved problems in knowledge management. A solution is required when the ontology schema evolves in the life cycle and the assertions have to be transferred to the newer version. The problem may become more complex in distributed settings when, for example, several autonomous software entities use and exchange partial assertional knowledge in a domain that is formalized by different though semantically overlapping descriptive theories. Such an exchange is essentially the migration of the assertional part of an ontology to other ontologies belonging to or used by different entities. The paper presents our method and tool for migrating instances between the ontologies that have structurally different but semantically overlapping schemas. The approach is based on the use of the manually coded transformation rules describing the changes between the input and the output ontologies. The tool is implemented as a plug-in for the ProjectNavigator prototype software framework. The article also reports the results of our three evaluation experiments. In these experiments we evaluated the degree of complexity in the structural changes to which our approach remains valid. We also chose the ontology sets in one of the experiments to make the results comparable with the ontology alignment software. Finally we checked how well our approach scales with the increase of the quantity of the migrated ontology instances to the numbers that are characteristic to industrial ontologies. In our opinion the evaluation results are satisfactory and suggest some directions for the future work.

Resources like ontologies are used in a number of applications, including natural language processing, information retrieval(especially from the Internet). Different methods have been proposed to build such resources. This paper proposes a new method to extract information from the Web to build a taxonomy of terms and Web resources for a given domain. Firstly, a (CHIR) method is used to identify candidat terms. Then a similarity (SIM) measure is introduced to select relevant concepts to build the ontology. Our new algorithm, called (CHIRSIM), is easy to implement and can be efficiently integrated into an information retrieval system to help improve the retrieval performance. Experimental results show that the proposed approach can effectively and efficiently construct a cancer domain ontology from unstructured text documents.