Necessary Conditions of Two-Level Uncertainty Reasoning Model (URM) and the Improvement on It

Based on the previous work, some necessary conditions of the two-level Uncertainty Reasoning Model (URM) are proposed and an improvement on the twcalevel Uan is made that can describe and process the deviation. In addition, the paper presents two theorems for specifying the correctness about the improvement. Finally, the application of the twrvlevel URM is discussed.

Air target intention recognition and causal effect analysis combining uncertainty information reasoning and potential outcome framework

Recognizing target intent is crucial for making decisions on the battlefield.However,the imperfect and ambiguous character of battlefield situations challenges the validity and causation analysis of classical intent recognition techniques.Facing with the challenge,a target intention causal analysis paradigm is proposed by combining with an Intervention Retrieval(IR)model and a Hybrid Intention Recognition(HIR)model.The target data acquired by the sensors are modelled as Basic Probability Assignments(BPAs)based on evidence theory to create uncertain datasets.Then,the HIR model is utilized to recognize intent for a tested sample from uncertain datasets.Finally,the intervention operator under the evidence structure is utilized to perform attribute intervention on the tested sample.Data retrieval is performed in the sample database based on the IR model to generate the intention distribution of the pseudo-intervention samples to analyze the causal effects of individual sample attributes.The simulation results demonstrate that our framework successfully identifies the target intention under the evidence structure and goes further to analyze the causal impact of sample attributes on the target intention.

Uncertain Knowledge Reasoning Based on the Fuzzy Multi Entity Bayesian Networks

With the rapid development of the semantic web and the ever-growing size of uncertain data,representing and reasoning uncertain information has become a great challenge for the semantic web application developers.In this paper,we present a novel reasoning framework based on the representation of fuzzy PR-OWL.Firstly,the paper gives an overview of the previous research work on uncertainty knowledge representation and reasoning,incorporates Ontology into the fuzzy Multi Entity Bayesian Networks theory,and introduces fuzzy PR-OWL,an Ontology language based on OWL2.Fuzzy PROWL describes fuzzy semantics and uncertain relations and gives grammatical definition and semantic interpretation.Secondly,the paper explains the integration of the Fuzzy Probability theory and the Belief Propagation algorithm.The influencing factors of fuzzy rules are added to the belief that is propagated between the nodes to create a reasoning framework based on fuzzy PR-OWL.After that,the reasoning process,including the SSFBN structure algorithm,data fuzzification,reasoning of fuzzy rules,and fuzzy belief propagation,is scheduled.Finally,compared with the classical algorithm from the aspect of accuracy and time complexity,our uncertain data representation and reasoning method has higher accuracy without significantly increasing time complexity,which proves the feasibility and validity of our solution to represent and reason uncertain information.

QUALITATIVE REASONING BY COMPUTING WITH WORDS IN HERRERA-MARTíNEZ’S LINGUISTIC MODEL

Most of modern systems for information retrieval, fusion and management have to deal with more and more qualitative information （by linguistic labels） besides information expressed quantitatively （by numbers）, since human reports are better and easier expressed in natural language than with numbers. In this paper, Herrera-Martfnez＇s 2-Tuple linguistic representation model is extended for reasoning with uncertain and qualitative information in Dezert-Smarandache Theory （DSmT） framework, in order to overcome the limitations of current approaches, i.e., the lack of precision in the final results of linguistic information fusion according to 1-Tuple representation （ q1 ）- The linguistic information which expresses the expert＇s qualitative beliefs is expressed by means of mixed 2 Tuples （equidistant linguistic labels with a numeric biased value）. Together with the 2-Tuple representation model, some basic operators are presented to carry out the fusion operation among qualitative information sources. At last, through simple example how 2-Tuple qualitative DSmT-based （q2 DSmT） fusion rules can be used for qualitative reasoning and fusion under uncertainty, which advantage is also showed by comparing with other methods.

Dempster-Shafer Evidence Theory and Study of Some Key Problems

As one of the most important mathematical methods, the Dempster-Shafer（D-S）evidence theory has been widely used in date fusion, risk assessment, target identification, knowledge reasoning,and other fields. This paper summarized the development and recent studies of the explanations of D-S model, evidence combination algorithms, and the improvement of the conflict during evidence combination, and also compared all explanation models,algorithms, improvements, and their applicable conditions. We are trying to provide a reference for future research and applications through this summarization.

A Fuzzy Knowledge Based System in Situation and Threat Assessment

This paper examines the use in assisting naval operators in managing the of a fuzzy knowledge-based system （KBS） situation and threat assessment problem in a littoral environment. Under the scenario created involving air contacts in a littoral environment, the assessment of the KBS model is conducted and the results show that the correlation between assessment and human operation officers＇ judgment was high. The fuzzy KBS with its corresponding toolkits is much more flexible to be integrated with other components of a command and control system, while the fine-tuning of membership functions and fuzzy inference rules are intensive, this makes any further modification a non-trivial task.