Trusting Others: A Pareto Distribution of Source and Message Credibility Among News Reporters

This study uses the case study of journalists to explore the socio-cognitive nature of interpersonal trust in growingly deceptive ecosystems. Journalists are ideal test subjects to explore these issues as professional trust allocators, who receive immediate feedback on right and wrong trust decisions. The study differentiates, for the first time, between source and message credibility evaluations, based on a combination of qualitative and quantitative methods. Findings show that journalists can distinguish source and message credibility. However, in practice they rely on source evaluations as an “autopilot” default mode, shifting gears to observations of source and message credibility in epistemically complex cases. The proportion between both is close to Pareto distribution. This extreme division challenges both inductive and mixed inference theories of epistemic trust and suggests revisiting the “typification” doctrine of newswork. Data partially support the hegemony and “epistemic injustice” theory, showing that traditional credibility criteria might trigger the exclusion of nontraditional voices.

Parallel AND/OR Search for Marginal MAP

Marginal MAP is a difficult mixed inference task for graphical models. Existing state-of-the-art algorithms for solving exactly this task are based on either depth-first or best-first sequential search over an AND/OR search space. In this paper, we explore and evaluate for the first time the power of parallel search for exact Marginal MAP inference. We introduce a new parallel shared-memory recursive best-first AND/OR search algorithm that explores the search space in a best-first manner while operating with limited memory. Subsequently, we develop a complete parallel search scheme that only parallelizes the conditional likelihood computations. We also extend the proposed algorithms into depth-first parallel search schemes. Our experiments on difficult benchmarks demonstrate the effectiveness of the parallel search algorithms against current sequential methods for solving Marginal MAP exactly.

AND/OR Search for Marginal MAP

Mixed inference such as the marginal MAP query (some variables marginalized by summation and others by maximization) is key to many prediction and decision models. It is known to be extremely hard; the problem is NPPP-complete while the decision problem for MAP is only NP-complete and the summation problem is #P-complete. Consequently, approximation anytime schemes are essential. In this paper, we show that the framework of heuristic AND/OR search, which exploits conditional independence in the graphical model, coupled with variational-based mini-bucket heuristics can be extended to this task and yield powerful state-of-the-art schemes. Specifically, we explore the complementary properties of best-first search for reducing the number of conditional sums and providing time-improving upper bounds, with depth-first search for rapidly generating and improving solutions and lower bounds. We show empirically that a class of solvers that interleaves depth-first with best-first schemes emerges as the most competitive anytime scheme.

Study of Intelligent Diagnosis System for Photoelectric Tracking Devices Based on Multiple Knowledge Representation

Aiming at the fatal flaws of the traditional diagnosis methods for the large-scale photoelectric tracking devices, such as poor stability and adaptive capacity, lack of inspiration and narrow domain knowledge of expert system, etc, more importantly, fundamentally improve the diagnostic efficiency and universality, in this paper, an intelligent mixed inference diagnosis expert system based on multiple knowledge representation and BP neural network is put forward. Firstly, some related key basic concepts and principles of intelligent fault diagnosis technology and several major applied diagnosis knowledge representation methods such as diagnosis fault tree, frame representation production rule and so on, were elaborated. Secondly, in view of high concurrency and relevancy of the system faults, a mixed reasoning mechanism combining BPNN and ES was researched. Finally, some interrelated essential implementation techniques, such as system architecture and VR technology, were also presented. Actual applications and experiments demonstrate that the proposed approach is robust and effective.

Some Preliminary Results on the Stableness of Extended F-rule Systems

In this paper we shall investigate an extended version of F-rule systems, in which each F-rule can include an arbitrary combination of disjunctions and conjunctions of atoms in the premise. The first main result here is a way to determine values assigned to these extended facts, based on two basic operators ⊕ and x;, which are shown to be equivalent to external probabilistic reasoning by resolving linear programming problem. Based on this, a definition on mixed inference operator for extended F-rule systems is discussed. We have shown that an extended F-rule system with the defined reasoning operator is stable iff its corresponding F-rule system is stable. This proposition allows us to apply all our available research results on F-rule systems to extended F-rule systems.