Finite-Time Observability of Probabilistic Logical Control Systems

This study investigates finite-time observability of probabilistic logical control systems(PLCSs)under three definitions(i.e.,finite-time observability with probability one,finite-time singleinput sequence observability with probability one,and finite-time arbitrary-input observability with probability one).The authors adopt a parallel extension technique to recast the finite-time observability problem of a PLCS as a finite-time set reachability problem.Then,the finite-time set reachability problem can be transferred to stabilization problem of a logic dynamical system by using the state transfer graph reconstruction method.Necessary and sufficient conditions for finite-time observability under the three definitions are derived respectively.Finally,the proposed methods are illustrated by numerical examples.

Agent-Oriented Probabilistic Logic Programming

Currently, agent-based computing is an active research area, and great efforts have been made towards the agent-oriented programming both from a theoretical and practical view. However, most of them assume that there is no uncertainty in agents＇ mental state and their environment. In other words, under this assumption agent developers are just allowed to specify how his agent acts when the agent is 100% sure about what is true/false. In this paper, this unrealistic assumption is removed and a new agent-oriented probabilistic logic programming language is proposed, which can deal with uncertain information about the world. The programming language is based on a combination of features of probabilistic logic programming and imperative programming.

A three-dimensional probabilistic fuzzy control system for network queue management

A novel probabilistic fuzzy control system is proposed to treat the congestion avoidance problem in transmission control protocol （TCP） networks. Studies on traffic measurement of TCP networks have shown that the packet traffic exhibits long range dependent properties called self-similarity, which degrades the network performance greatly. The probabilistic fuzzy control （PFC） system is used to handle the complex stochastic features of self-similar traffic and the modeling uncertainties in the network system. A three-dimensional （3-D） membership function （MF） is embedded in the PFC to express and describe the stochastic feature of network traffic. The 3-D MF has extended the traditional fuzzy planar mapping and further provides a spatial mapping among ＂fuzziness-randomness-state＂. The additional stochastic expression of 3-D MF provides the PFC an additional freedom to handle the stochastic features of self-similar traffic. Simulation experiments show that the proposed control method achieves superior performance compared to traditional control schemes in a stochastic environment.

New method for hybrid multiple attribute decision-making problems with decision maker's aspiration level

How can we account for decision maker's aspiration level in multiple attribute decision-making problems? This is a question that has recently gained much scholarly attention. This study draws on the prospect theory and the Dempster-Shafer theory to propose a method that can resolve this issue. First, the precise and interval numbers and linguistic labels are compared, and then, the decision matrix is transformed into a prospect decision matrix on the basis of decision maker's aspiration level. Second, the Dempster-Shafer theory is applied to aggregate the prospect values of all alternatives on each attribute using their prospect belief intervals. Third, the orders of alternatives are obtained for all decision methods by comparing prospect belief intervals. Final, the effectiveness and feasibility of the proposed method are demonstrated by using an illustrative example. ? 1990-2011 Beijing Institute of Aerospace Information.

Model Checking for Probabilistic Multiagent Systems

In multiagent systems,agents usually do not have complete information of the whole system,which makes the analysis of such systems hard.The incompleteness of information is normally modelled by means of accessibility relations,and the schedulers consistent with such relations are called uniform.In this paper,we consider probabilistic multiagent systems with accessibility relations and focus on the model checking problem with respect to the probabilistic epistemic temporal logic,which can specify both temporal and epistemic properties.However,the problem is undecidable in general.We show that it becomes decidable when restricted to memoryless uniform schedulers.Then,we present two algorithms for this case:one reduces the model checking problem into a mixed integer non-linear programming(MINLP)problem,which can then be solved by Satisfiability Modulo Theories(SMT)solvers,and the other is an approximate algorithm based on the upper confidence bounds applied to trees(UCT)algorithm,which can return a result whenever queried.These algorithms have been implemented in an existing model checker and then validated on experiments.The experimental results show the efficiency and extendability of these algorithms,and the algorithm based on UCT outperforms the one based on MINLP in most cases.

Orthogonal Algorithm of Logic Probability and Syndrome-Testable Analysis

A new method,orthogonal algorithm,is presented to compute the logic probabilities(i.e.signal probabili- ties)accurately.The transfer properties of logic probabilities are studied first,which are useful for the calcula- tion of logic probability of the circuit with random independent inputs.Then the orthogonal algorithm is des- cribed to compute the logic probability of Boolean function realized by a combinational circuit.This algorithm can make Boolean function“ORTHOGONAL”so that the logic probabilities can be easily calculated by summing up the logic probabilities of all orthogonal terms of the Boolean function.

A survey on temporal logics for specifying and verifying real-time systems

Over the last two decades, there has been an extensive study of logical formalisms on specifying and verifying real-time systems. Temporal logics have been an important research subject within this direction. Although numerous logics have been introduced for formal specification of real-time and complex systems, an up to date survey of these logics does not exist in the literature. In this paper we analyse various temporal formalisms introduced for specification, including propositional/first-order linear temporal logics, branching temporal logics, interval temporal logics, real-time temporal logics and probabilistic temporal logics. We give decidability, axiomatizability, expressiveness, model checking results for each logic analysed. We also provide a comparison of features of the temporal logics discussed.

Nondeterministic Probabilistic Petri Net -- A New Method to Study Qualitative and Quantitative Behaviors of System

There are many variants of Petri net at present, and some of them can be used to model system with both function and performance specification, such as stochastic Petri net, generalized stochastic Petri net and probabilistic Petri net. In this paper, we utilize extended Petri net to address the issue of modeling and verifying system with probability and nondeterminism besides function aspects. Using probabilistic Petri net as reference, we propose a new mixed model NPPN （Nondeterministic Probabilistic Petri Net） system, which can model and verify systems with qualitative and quantitative behaviours. Then we develop a kind of process algebra for NPPN system to interpret its algebraic semantics, and an action- based PCTL （Probabilistic Computation Tree Logic） to interpret its logical semantics. Afterwards we present the rules for compositional operation of NPPN system based on NPPN system process algebra, and the model checking algorithm based on the action-based PCTL. In order to put the NPPN system into practice, we develop a friendly and visual tool for modeling, analyzing, simulating, and verifying NPPN system using action-based PCTL. The usefulness and effectiveness of the NPPN system are illustrated by modeling and model checking an elaborate model of travel arrangements workflow.

A Modeling and Probabilistic Reasoning Method of Dynamic Uncertain Causality Graph for Industrial Fault Diagnosis

Online automatic fault diagnosis in industrial systems is essential for guaranteeing safe, reliable and efficient operations.However, difficulties associated with computational overload, ubiquitous uncertainties and insufficient fault samples hamper the engineering application of intelligent fault diagnosis technology. Geared towards the settlement of these problems, this paper introduces the method of dynamic uncertain causality graph, which is a new attempt to model complex behaviors of real-world systems under uncertainties. The visual representation to causality pathways and self-relied "chaining" inference mechanisms are analyzed. In particular, some solutions are investigated for the diagnostic reasoning algorithm to aim at reducing its computational complexity and improving the robustness to potential losses and imprecisions in observations. To evaluate the effectiveness and performance of this method, experiments are conducted using both synthetic calculation cases and generator faults of a nuclear power plant. The results manifest the high diagnostic accuracy and efficiency, suggesting its practical significance in large-scale industrial applications.

Ontology-Based Context-Aware Middleware for Smart Spaces

Context-awareness enhances human-centric, intelligent behavior in a smart environment; however context-awareness is not widely used due to the lack of effective infrastructure to support context-aware applications. This paper presents an agent-based middleware for providing context-aware services for smart spaces to afford effective support for context acquisition, representation, interpretation, and utilization to applications. The middleware uses a formal context model, which combines first order probabilistic logic （FOPL） and web ontology language （OWL） ontologies, to provide a common understanding of contextual information to facilitate context modeling and reasoning about imperfect and ambiguous contextual information and to enable context knowledge sharing and reuse. A context inference mechanism based on an extended Bayesian network approach is used to enable automated reactive and deductive reasoning. The middleware is used in a case study in a smart classroom, and performance evaluation result shows that the context reasoning algorithm is good for non-time-critical applications and that the complexity is highly sensitive to the size of the context dataset.

Fusion of visible and infrared imagery for face recognition

In recent years face recognition has received substantial attention, but still remained very challenging in real applications. Despite the variety of approaches and tools studied, face recognition is not accurate or robust enough to be used in uncontrolled environments. Infrared (IR) imagery of human faces offers a promising alternative to visible imagery, however, IR has its own limitations. In this paper, a scheme to fuse information from the two modalities is proposed. The scheme is based on eigenfaces and probabilistic neural network (PNN), using fuzzy integral to fuse the objective evidence supplied by each modality. Recognition rate is used to evaluate the fusion scheme. Experimental results show that the scheme improves recognition performance substantially.