139,089 research outputs found
Affectively Aligned Cognitive Assistance Using Bayesian Affect Control Theory
Abstract. This paper describes a novel emotionally intelligent cognitive assistant to engage and help older adults with Alzheimerâs disease (AD) to complete activities of daily living (ADL) more independently. Our new system combines two research streams. First, the development of cognitive assistants with artificially intelligent controllers using partially observable Markov decision processes (POMDPs). Second, a model of the dynamics of emotion and identity called Affect Control Theory that arises from the sociological literature on culturally shared sentiments. We present background material on both of these research streams, and then demonstrate a prototype assistive technology that combines the two. We discuss the affective reasoning, the probabilistic and decision-theoretic reasoning, the computer-vision based activity monitoring, the embodied prompting, and we show results in proof-of-concept tests.
Take another little piece of my heart: a note on bridging cognition and emotions
Science urges philosophy to be more empirical and philosophy urges science to be more reflective. This markedly occurred along the âdiscovery of the artificialâ (CORDESCHI 2002): in the early days of Cybernetics and Artificial Intelligence (AI) researchers aimed at making machines more cognizant while setting up a framework to better understand human intelligence.
By and large, those genuine goals still hold today, whereas AI has become more concerned with specific aspects of intelligence, such as (machine) learning, reasoning, vision, and action. As a matter of fact, the field suffers from a chasm between two formerly integrated aspects. One is the engineering endeavour involving the development of tools, e.g., autonomous systems for driving cars as well as software for semantic information retrieval. The other is the philosophical debate that tries to answer questions concerning the nature of intelligence. Bridging these two levels can indeed be crucial in developing a deeper understanding of minds.
An opportunity might be offered by the cogent theme of emotions. Traditionally, computer science, psychological and philosophical research have been compelled to investigate mental processes that do not involve mood, emotions and feelings, in spite of Simonâs early caveat (SIMON 1967) that a general theory of cognition must incorporate the influences of emotion.
Given recent neurobiological findings and technological advances, the time is ripe to seriously weigh this promising, albeit controversial, opportunity
Herding and Social Pressure in Trading Tasks: A Behavioural Analysis
We extend the experimental literature on Bayesian herding using evidence from a financial decision-making experiment. We identify significant propensities to herd increasing with the degree of herd-consensus. We test various herding models to capture the differential impacts of Bayesian-style thinking versus behavioural factors. We find statistically significant associations between herding and individual characteristics such as age and personality traits. Overall, our evidence is consistent with explanations of herding as the outcome of social and behavioural factors. Suggestions for further research are outlined and include verifying these findings and identifying the neurological correlates of propensities to herd
Bayesian feedback versus Markovian feedback in a two-level atom
We compare two different approaches to the control of the dynamics of a
continuously monitored open quantum system. The first is Markovian feedback as
introduced in quantum optics by Wiseman and Milburn [Phys. Rev. Lett. {\bf 70},
548 (1993)]. The second is feedback based on an estimate of the system state,
developed recently by Doherty {\em et al.} [Phys. Rev. A {\bf 62}, 012105
(2000)]. Here we choose to call it, for brevity, {\em Bayesian feedback}. For
systems with nonlinear dynamics, we expect these two methods of feedback
control to give markedly different results. The simplest possible nonlinear
system is a driven and damped two-level atom, so we choose this as our model
system. The monitoring is taken to be homodyne detection of the atomic
fluorescence, and the control is by modulating the driving. The aim of the
feedback in both cases is to stabilize the internal state of the atom as close
as possible to an arbitrarily chosen pure state, in the presence of inefficient
detection and other forms of decoherence. Our results (obtain without recourse
to stochastic simulations) prove that Bayesian feedback is never inferior, and
is usually superior, to Markovian feedback. However it would be far more
difficult to implement than Markovian feedback and it loses its superiority
when obvious simplifying approximations are made. It is thus not clear which
form of feedback would be better in the face of inevitable experimental
imperfections.Comment: 10 pages, including 3 figure
Bayesian Learning Models of Pain: A Call to Action
Learning is fundamentally about action, enabling the successful navigation of a changing and uncertain environment. The experience of pain is central to this process, indicating the need for a change in action so as to mitigate potential threat to bodily integrity. This review considers the application of Bayesian models of learning in pain that inherently accommodate uncertainty and action, which, we shall propose are essential in understanding learning in both acute and persistent cases of pain
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