Probabilistic Multiagent Reasoning over Annotated Amalgamated F-Logic Ontologies

Probabilistic Multiagent Reasoning over Annotated Amalgamated F-Logic Ontologies

In a multiagent system (MAS), agents can have different opinions about a given problem. In order to solve the problem collectively they have to reach consensus about the ontology of the problem. A solution to probabilistic reasoning in such an environment by using a social network of trust is given. It is shown that frame logic can be annotated and amalgamated by using this approachwhich gives ...

Reasoning with Annotated Description Logic Ontologies

Logic Programming Techniques for Reasoning with Probabilistic Ontologies

The increasing popularity of the Semantic Web drove to a widespread adoption of Description Logics (DLs) for modeling real world domains. To help the diffusion of DLs, a large number of reasoning algorithms have been developed. Usually these algorithms are implemented in procedural languages such as Java or C++. Most of the reasoners exploit the tableau algorithm which has to manage non-determi...

Reasoning with Probabilistic Ontologies

Modeling real world domains requires ever more frequently to represent uncertain information. The DISPONTE semantics for probabilistic description logics allows to annotate axioms of a knowledge base with a value that represents their probability. In this paper we discuss approaches for performing inference from probabilistic ontologies following the DISPONTE semantics. We present the algorithm...

Probabilistic Reasoning in Multiagent Systems

This book investigates the opportunities in building intelligent decision support systems offered by multiagent distributed probabilistic reasoning. Probabilistic reasoning with graphical models, also known as Bayesian networks or belief networks, has become an active field of research and practice in artificial intelligence, operations research, and statistics in the past two decades. The succ...