Probabilistic automata exhibit both probabilistic and non-deterministic choice. They are therefore a powerful semantic foundation for modeling concurrent systems with random phenomena arising in many applications ranging from artificial intelligence, security, systems biology to performance modeling. Several variations of bisimulation and simulation relations have proved to be useful as means t...

Decision Support Systems for the environment have to incorporate and exploit knowledge about the phenomena and the interdependencies in the affected natural systems, i.e. a model. We discuss different tasks and requirements, provide a logical formalization of the tasks, and propose to use methods and techniques developed in research on modeling and model-based systems in Artificial Intelligence...

DIKW hierarchy is the model used for discussion of data, information, knowledge, wisdom and their interrelationships. However, definitions of data, information, and knowledge are entrapped in a logical fallacy known as circular definition. There are some agreements in the definitions without the fallacy. The missing ‘Intelligence’ in DIKW plays a major role between knowledge and wisdom. This gi...

The automated understanding and generation of music is an area of research that raises problems that are central to the Artiicial Intelligence enterprise. Recent work at Edinburgh has aimed to use a symbolic AI approach for this eld. We indicate how a more abstract understanding of music representation uniies this approach, and how logical descriptions of hierarchical structures can be incorpor...

Classical artificial intelligence (CAI) and embodied cognition (EC) were successfully applied in different areas: CAI has its strength in fields such as planning or high-level cognition, which require a precise computation that involves logical inference; EC produced excellent approaches for sensori-motor coupling, which requires a robust and flexible computation. The question that we are follo...

Using mathematical morphology on formulas introduced recently by Bloch and Lang (Proceedings of IPMU’2000) we define two new explanatory relations. Their logical behavior is analyzed. The results show that these natural ways for defining preferred explanations are robust because these relations satisfy almost all postulates of explanatory reasoning introduced by Pino-Pérez and Uzcátegui (Artifi...

This paper explores the relationship between fact mutability, intervention and human evaluation of counterfactual conditionals. Two experiments are reported that show the effects of causal strength and causal distance on fact mutability and intervention. Subjects’ answers are compared to the predictions of three models of counterfactual reasoning in Artificial Intelligence. This comparison demo...

Relational learning is a subfield of artificial intelligence, that learns with expressive logical or relational representations. In this thesis, I consider the problem of efficient relational learning. I describe a new relational learning approach based on path-constrained random walks, and demonstrate, with extensive experiments on IR and NLP tasks, how relational learning can be applied at a ...

The uncertainties implicit in intelligence gathering are not only about the state of the world, but also about the ways in which varying contexts should affect the degree to which a proposition is believed. We call this latter form of uncertainty higher order uncertainty, and argue that the introduction of a logical operator to K. Laskey’s MEBN specification can allow for learning about such un...