Information sharing between heterogeneous uncertain reasoning models in a multi-agent environment : a case study

The issue of information sharing and exchanging is one of the most important issues in the areas of artificial intelligence and knowledge-based systems (KBSs), or even in the broader areas of computer and information technology. This paper deals with a special case of this issue by carrying out a case study of information sharing between two well-known heterogeneous uncertain reasoning models: the certainty factor model and the subjective Bayesian method. More precisely, this paper discovers a family of exactly isomorphic transformations between these two uncertain reasoning models. More interestingly, among isomorphic transformation functions in this family, different ones can handle different degrees to which a domain expert is positive or negative when performing such a transformation task. The direct motivation of the investigation lies in a realistic consideration. In the past, expert systems exploited mainly these two models to deal with uncertainties. In other words, a lot of stand-alone expert systems which use the two uncertain reasoning models are available. If there is a reasonable transformation mechanism between these two uncertain reasoning models, we can use the Internet to couple these pre-existing expert systems together so that the integrated systems are able to exchange and share useful information with each other, thereby improving their performance through cooperation. Also, the issue of transformation between heterogeneous uncertain reasoning models is significant in the research area of multi-agent systems because different agents in a multi-agent system could employ different expert systems with heterogeneous uncertain reasonings for their action selections and the information sharing and exchanging is unavoidable between different agents. In addition, we make clear the relationship between the certainty factor model and probability theory.

A neural network-based multi-agent classifier system with a Bayesian formalism for trust measurement

In this paper, a neural network (NN)-based multi-agent classifier system (MACS) utilising the trust-negotiation-communication (TNC) reasoning model is proposed. A novel trust measurement method, based on the combination of Bayesian belief functions, is incorporated into the TNC model. The Fuzzy Min-Max (FMM) NN is used as learning agents in the MACS, and useful modifications of FMM are proposed so that it can be adopted for trust measurement. Besides, an auctioning procedure, based on the sealed bid method, is applied for the negotiation phase of the TNC model. Two benchmark data sets are used to evaluate the effectiveness of the proposed MACS. The results obtained compare favourably with those from a number of machine learning methods. The applicability of the proposed MACS to two industrial sensor data fusion and classification tasks is also demonstrated, with the implications analysed and discussed.

Special issue : Computational intelligence models for image processing and information reasoning

Computational Intelligence (CI) models comprise robust computing methodologies with a high level of machine learning quotient. CI models, in general, are useful for designing computerized intelligent systems/machines that possess useful characteristics mimicking human behaviors and capabilities in solving complex tasks, e.g., learning, adaptation, and evolution. Examples of some popular CI models include fuzzy systems, artificial neural networks, evolutionary algorithms, multi-agent systems, decision trees, rough set theory, knowledge-based systems, and hybrid of these models. This special issue highlights how different computational intelligence models, coupled with other complementary techniques, can be used to handle problems encountered in image processing and information reasoning.

The role of fear of self and responsibility in obsessional doubt processes: a bayesian hierarchical model

The literature concerning obsessive-compulsive disorder (OCD) indicates that obsessions frequently imply negative evaluative beliefs regarding the self. The construct of the feared self has been used to describe the set of harmful attributes an individual worries they may possess. This study aimed to partially replicate previous research that demonstrated a relationship between feared-self beliefs and obsessional doubt in OCD-relevant contexts. The relationship between perceptions of personal responsibility and associated levels of doubt was also examined. Nonclinical participants (N = 221; 155 female; Mage = 26.4, SD = 9.2) were presented with vignettes related to checking and non OCD-relevant themes, which quantified doubt through the presentation of alternating reality-based (i.e., sensory) and possibility-based information. Of the total sample, 112 participants were randomly allocated to a personally relevant condition (in which the action implied in the vignettes was completed by the reader), and 109 were allocated to a second, other-relevant, condition (in which the action implied in the vignettes was completed by a proximal other). The results provided support for reasoning processes implicated in OCD, suggesting that feared-self beliefs may partially contribute to heightened levels of doubt in response to possibility vs. reality-based information in OCD-relevant contexts. Personal relevance contributed to greater baseline levels of doubt, but not to greater responses to the reality- and possibility-based statements accompanying the OCD-relevant vignette. Implications for theory and future research are discussed.