Received: 23 March 2011 Revised: 10 September 2011 2nd Revision: 14 November 2011 3rd Revision: 17 December 2011 Accepted: 19 December 2011 Abstract Information Systems enjoyment has been identified as a desirable phenomenon, because it can drive various aspects of system use. In this study, we argue that it can also be a key ingredient in the formation of adverse outcomes, such as technology-r...

Human history has been marked by social instability and conflict, often driven by the irreconcilability of opposing sets of beliefs, ideologies, and religious dogmas. The dynamics of belief systems has been studied mainly from two distinct perspectives, namely how cognitive biases lead to individual belief rigidity and how social influence leads to social conformity. Here we propose a unifying ...

We present procedures and experimental results in constructing belief networks for image classi cation systems based on probabilistic reasoning. In particular, we compare the performance of systems based on manually constructed and automatically constructed belief networks. The systems exploit existing image descriptions and also exploit interactions between multiple classi ers to improve class...

Received: 8 April 2008 Revised: 15 August 2008 2nd Revision: 18 January 2009 Accepted: 23 February 2009 Abstract Information security has become increasingly important to organizations. Despite the prevalence of technical security measures, individual employees remain the key link – and frequently the weakest link – in corporate defenses. When individuals choose to disregard security policies a...

The theory of belief networks offers a relatively new approach for dealing with uncertain information in knowledge-based (expert) systems. In contrast with the heuristic techniques for reasoning with uncertainty employed in many rule-based expert systems, the theory of belief networks is mathematically sound, based on techniques from probability theory. It therefore seems attractive to convert ...

This paper presents a temporal belief logic called LTB. In addition to the usual connectives of linear discrete temporal logic, LTB contains an indexed set of modal belief connectives,via which it is possible to represent the belief systems of resource-bounded reasoning agents. The applications of LTB in general, and its use for representing the dynamic properties of multi-agent AI systems in p...

Perceptual and control systems are tasked with the challenge of accurately and efficiently estimating the dynamic states of objects in the environment. To properly account for uncertainty, it is necessary to maintain a dynamical belief state representation rather than a single state vector. In this review, canonical algorithms for computing and updating belief states in robotic applications are...

Belief revision theory studies the impact of acquiring new information. It is a fundamental activity of human intelligence, and it defines an exciting and significant research area in philosophy, logic and computer science (AI). One of the hallmarks of intelligence is adaptability, and belief revision theory provides sound modellings for an important kind of cognitive process, namely, changes o...