According to probabilistic theories of higher cognition, beliefs come in degrees. Here, we test this idea by studying how people make predictions from uncertain beliefs. According to the degrees-of-belief theory, people should take account of both highand low-probability beliefs when making predictions that depend on which of those beliefs are true. In contrast, according to the all-or-none the...

Business processes consist of sets of individual and collaborative activities performed by agents (human of artificial) and the interactions between them. The agents have their own local beliefs and expectations about the behaviour(s) of the other agents. We represent these beliefs by using the ‘interaction belief’ concept. We show how a designer can reason about an interaction belief, how it c...

An intelligent agent must be able to change its beliefs in a rational manner and with a plausible outcome. While the central belief change theories have well-developed formal characterisations, there has been limited work on applying these theories in a practical setting. In this paper we examine belief base contraction, specifically kernel contraction, in the framework of the description logic...

This paper presents a plausible reasoning sys­ tem to illustrate some broad issues in knowl­ edge representation: dualities between dif­ ferent reasoning forms, the difficulty of uni­ fying complementary reasoning styles, and the approximate nature of plausible reason­ ing. These issues have a common underly­ ing theme: there should be an underlying belief calculus of which the many different r...

In this paper an adaptive integrative ambient agent model is introduced incorporating the estimation of a human’s interactive dynamics of believing and feeling. To this end the integrative agent model is equipped with a dynamical model which describes how the strength of a belief depends both on information obtained and emotional responses on the belief. Both the strength of the belief and of t...

Biologically inspired artificial neural networks have been widely used for machine learning tasks such as object recognition. Deep architectures, such as the Convolutional Neural Network, and the Deep Belief Network have recently been implemented successfully for object recognition tasks. We conduct experiments to test the hypothesis that certain primarily generative models such as the Deep Bel...

While belief tracking is known to be important in allowing statistical dialog systems to manage dialogs in a highly robust manner, until recently little attention has been given to analysing the behaviour of belief tracking techniques. The Dialogue State Tracking Challenge has allowed for such an analysis, comparing multiple belief tracking approaches on a shared task. Recent success in using d...

Maintaining an accurate set of beliefs in a partially observable scenario, particularly with respect to other agents operating in the same space, is a vital aspect of multiagent planning. We analyze how the beliefs of an agent can be updated for fast adaptivity to changes in the behavior of an unknown teammate. The main contribution of this paper is the empirical evaluation of an agent cooperat...