Abduction in Annotated Probabilistic Temporal Logic

Annotated Probabilistic Temporal (APT) logic programs are a form of logic programs that allow users to state (or systems to automatically learn)rules of the form ``formula G becomes true K time units after formula F became true with L to U% probability.\u27\u27 In this paper, we develop a theory of abduction for APT logic programs. Specifically, given an APT logic program Pi, a set of formulas H that can be ``added\u27\u27 to Pi, and a goal G, is there a subset S of H such that Pi cup S is consistent and entails the goal G? In this paper, we study the complexity of the Basic APT Abduction Problem (BAAP). We then leverage a geometric characterization of BAAP to suggest a set of pruning strategies when solving BAAP and use these intuitions to develop a sound and complete algorithm

Model-based dependability analysis : state-of-the-art, challenges and future outlook

Abstract: Over the past two decades, the study of model-based dependability analysis has gathered significant research interest. Different approaches have been developed to automate and address various limitations of classical dependability techniques to contend with the increasing complexity and challenges of modern safety-critical system. Two leading paradigms have emerged, one which constructs predictive system failure models from component failure models compositionally using the topology of the system. The other utilizes design models - typically state automata - to explore system behaviour through fault injection. This paper reviews a number of prominent techniques under these two paradigms, and provides an insight into their working mechanism, applicability, strengths and challenges, as well as recent developments within these fields. We also discuss the emerging trends on integrated approaches and advanced analysis capabilities. Lastly, we outline the future outlook for model-based dependability analysis

Using Probability to Reason about Soft Deadlines

Soft deadlines are significant in systems in which a bound on the response time is important, but the failure to meet the response time is not a disaster. Soft deadlines occur, for example, in telephony and switching networks. We investigate how to put probabilistic bounds on the time-complexity of a concurrent logic program by combining (on-line) profiling with an (off-line) probabilistic complexity analysis. The profiling collects information on the likelihood of case selection and the analysis uses this information to infer the probability of an agent terminating within k steps. Although the approach does not reason about synchronization, we believe that its simplicity and good (essentially quadratic) complexity mean that it is a promising first step in reasoning about soft deadlines