This paper attempts to present the Taoist understanding of evil. In the Taoist tradition, especially in Tao Te Ching, evil is divided into two categories: causal evil and consequential evil. Causal evils are those evils that are said to be the causes of other evils; consequential evils are those that are said to be the consequences of the causal evils. Causal evils originate from human will, an...

We start from two closure operators defined on the elements of a special kind of partially ordered sets, called causal nets. Causal nets are used to model histories of concurrent processes, recording occurrences of local states and of events. If every maximal chain (line) of such a partially ordered set meets every maximal antichain (cut), then the two closure operators coincide, and generate a...

This work proposes to put up a tool for diagnosing multi faults based on model using techniques of detection and localization inspired from the community of artificial intelligence and that of automatic. The diagnostic procedure to be integrated into the supervisory system must therefore be provided with explanatory features. Techniques based on causal reasoning are a pertinent approach for thi...

We are dealing with solving planning problems by the GraphPlan algorithm. We concentrate on solving a problem of finding supporting actions for a goal. This problem arises as a sub-problem many times during search for a solution. We showed in the paper that the supports problem is NP-complete. In order to improve the solving process of the supports problems we proposed a new global consistency ...

According to Laub and Sampson (2003), desistance from crime is made possible by “knifing off”—“offenders desist in response to structurally induced turning points that serve as the catalyst for sustaining long-term behavioral change” (p. 149). What turning points create are new situations that allow individuals to knife off the past, in part, by changing those routine activity patterns that led...

Correspondent inferences in attribution theory deal with assigning causes to behaviour based on true dispositions rather than situational factors. In this paper, we investigate how knowledge representation tools in Artificial Intelligence (AI), such as Bayesian networks (BNs), can help represent such situations and distinguish between the types of clues used in assessing the behaviour (disposit...

While there has been considerable research in learning Bayesian network structure from data, until recently most of this research assumed that every variable of interest may be jointly measured in a single dataset. In practice, however, it is often the case that researchers only have access to data that is distributed across multiple datasets, which share some variables, but have other unique v...

In this paper, we show how different behavioral consistency concepts can be formed for sequence diagrams and statecharts. Our approach relies on the partial translation of models into a semantic domain and on the definition of explicit consistency conditions. Partial translations and consistency conditions can be combined to form an explicit consistency concept. In order to make our approach ap...

Bayesian networks are currently one of the most interesting techniques of artificial intelligence. They combine the readability of a knowledge representation by an intuitive causal graph and the effectiveness of a data representation that takes into account the uncertainty in reasoning. They are used in various applications. This paper presents some ideas on the concept of Bayesian networks. We...