There are three kinds of uncertainty in the process of fish-disease diagnosis, such as randomicity, fuzzy and imperfection, which affect the veracity of fish-disease diagnostic conclusion. So, it is important to construct a fish-disease diagnostic model to effectively deal with…

Associating the pattern in text data with the pattern with time series data is a novel task. In this paper, an approach that utilizes the features of the time series data and domain knowledge is proposed and used to identify…

In this paper we present an e ective prover for mbC, a minimal inconsistency logic. The mbC logic is a paraconsistent logic of the family of logics of formal inconsistency. Paraconsistent logics have several philosophical motivations as well as many…

This paper proposes an original generic hierarchical framework in order to facilitate the modeling stage of complex autonomous robotics mission planning problems with action uncertainties. Such stochastic planning problems can be modeled as Markov Decision Processes [5]. This work is…

In the past, numerous approaches have been formulated either for approximating Pareto-optimal alternatives or supporting the decision making process with an interactive multi criteria decision aiding methodology. The article on the other hand presents an integrated system for the resolution…

Classical methods for representing and reasoning with knowledge rely on the assumption that the available information is complete, certain and consistent. In real-world problems this is usually not the case, and Al has long dealt with the issue of finding…

The design of intelligent agents is greatly influenced by the many different models that exist to represent knowledge. It is essential that such agents have computationally adequate mechanisms to manage its knowledge, which more often than not is incomplete and/or…

Multiagent systems and online communities rely on rating systems to infer the reputation given to an individual within a particular context. The notion of reputation is essential for helping a given individual to trust in other individuals and for being…

Co-training can learn from datasets having a small number of labelled examples and a large number of unlabelled ones. It is an iterative algorithm where examples labelled in previous iterations are used to improve the classification of examples from the…

Knowledge acquisition for systems based on knowledge can be called knowledge engineering, according to Feigenbaum (1980) who used the term to describe the reduction of a wide context of knowledge for a set of facts and rules. The term Knowledge…