107,649 research outputs found
A question of balance: The benefits of pattern-recognition when solving problems in a complex domain
This is the accepted manuscript version of the following article: M. Lloyd-Kelly, F. Gobet, and Peter C. R. Lane, “A Question of Balance The Benefits of Pattern-Recognition when Solving Problems in a Complex Domain”, LNCS Transactions on Computational Collective Intelligence, Vol. XX, 2015. The final published version is available at: http://www.springer.com/gb/book/9783319275420 © 2015 Springer International Publishing.The dual-process theory of human cognition proposes the existence of two systems for decision-making: a slower, deliberative,problem-solving system and a quicker, reactive, pattern-recognition system. We alter the balance of these systems in a number of computational simulations using three types of agent equipped with a novel, hybrid, human-like cognitive architecture. These agents are situated in the stochastic, multi-agent Tileworld domain, whose complexity can be precisely controlled and widely varied. We explore how agent performance is affected by different balances of problem-solving and pattern-recognition, and conduct a sensitivity analysis upon key pattern-recognition system variables. Results indicate that pattern-recognition improves agent performance by as much as 36.5 % and, if a balance is struck with particular pattern-recognition components to promote pattern-recognition use, performance can be further improved by up to 3.6 %. This research is of interest for studies of expert behaviour in particular, and AI in general.Peer reviewedFinal Accepted Versio
Building Machines That Learn and Think Like People
Recent progress in artificial intelligence (AI) has renewed interest in
building systems that learn and think like people. Many advances have come from
using deep neural networks trained end-to-end in tasks such as object
recognition, video games, and board games, achieving performance that equals or
even beats humans in some respects. Despite their biological inspiration and
performance achievements, these systems differ from human intelligence in
crucial ways. We review progress in cognitive science suggesting that truly
human-like learning and thinking machines will have to reach beyond current
engineering trends in both what they learn, and how they learn it.
Specifically, we argue that these machines should (a) build causal models of
the world that support explanation and understanding, rather than merely
solving pattern recognition problems; (b) ground learning in intuitive theories
of physics and psychology, to support and enrich the knowledge that is learned;
and (c) harness compositionality and learning-to-learn to rapidly acquire and
generalize knowledge to new tasks and situations. We suggest concrete
challenges and promising routes towards these goals that can combine the
strengths of recent neural network advances with more structured cognitive
models.Comment: In press at Behavioral and Brain Sciences. Open call for commentary
proposals (until Nov. 22, 2016).
https://www.cambridge.org/core/journals/behavioral-and-brain-sciences/information/calls-for-commentary/open-calls-for-commentar
Empiricism without Magic: Transformational Abstraction in Deep Convolutional Neural Networks
In artificial intelligence, recent research has demonstrated the remarkable potential of Deep Convolutional Neural Networks (DCNNs), which seem to exceed state-of-the-art performance in new domains weekly, especially on the sorts of very difficult perceptual discrimination tasks that skeptics thought would remain beyond the reach of artificial intelligence. However, it has proven difficult to explain why DCNNs perform so well. In philosophy of mind, empiricists have long suggested that complex cognition is based on information derived from sensory experience, often appealing to a faculty of abstraction. Rationalists have frequently complained, however, that empiricists never adequately explained how this faculty of abstraction actually works. In this paper, I tie these two questions together, to the mutual benefit of both disciplines. I argue that the architectural features that distinguish DCNNs from earlier neural networks allow them to implement a form of hierarchical processing that I call “transformational abstraction”. Transformational abstraction iteratively converts sensory-based representations of category exemplars into new formats that are increasingly tolerant to “nuisance variation” in input. Reflecting upon the way that DCNNs leverage a combination of linear and non-linear processing to efficiently accomplish this feat allows us to understand how the brain is capable of bi-directional travel between exemplars and abstractions, addressing longstanding problems in empiricist philosophy of mind. I end by considering the prospects for future research on DCNNs, arguing that rather than simply implementing 80s connectionism with more brute-force computation, transformational abstraction counts as a qualitatively distinct form of processing ripe with philosophical and psychological significance, because it is significantly better suited to depict the generic mechanism responsible for this important kind of psychological processing in the brain
Embodied interaction with visualization and spatial navigation in time-sensitive scenarios
Paraphrasing the theory of embodied cognition, all aspects of our cognition are determined primarily by the contextual information and the means of physical interaction with data and information. In hybrid human-machine systems involving complex decision making, continuously maintaining a high level of attention while employing a deep understanding concerning the task performed as well as its context are essential. Utilizing embodied interaction to interact with machines has the potential to promote thinking and learning according to the theory of embodied cognition proposed by Lakoff. Additionally, the hybrid human-machine system utilizing natural and intuitive communication channels (e.g., gestures, speech, and body stances) should afford an array of cognitive benefits outstripping the more static forms of interaction (e.g., computer keyboard). This research proposes such a computational framework based on a Bayesian approach; this framework infers operator\u27s focus of attention based on the physical expressions of the operators. Specifically, this work aims to assess the effect of embodied interaction on attention during the solution of complex, time-sensitive, spatial navigational problems. Toward the goal of assessing the level of operator\u27s attention, we present a method linking the operator\u27s interaction utility, inference, and reasoning. The level of attention was inferred through networks coined Bayesian Attentional Networks (BANs). BANs are structures describing cause-effect relationships between operator\u27s attention, physical actions and decision-making. The proposed framework also generated a representative BAN, called the Consensus (Majority) Model (CMM); the CMM consists of an iteratively derived and agreed graph among candidate BANs obtained by experts and by the automatic learning process. Finally, the best combinations of interaction modalities and feedback were determined by the use of particular utility functions. This methodology was applied to a spatial navigational scenario; wherein, the operators interacted with dynamic images through a series of decision making processes. Real-world experiments were conducted to assess the framework\u27s ability to infer the operator\u27s levels of attention. Users were instructed to complete a series of spatial-navigational tasks using an assigned pairing of an interaction modality out of five categories (vision-based gesture, glove-based gesture, speech, feet, or body balance) and a feedback modality out of two (visual-based or auditory-based). Experimental results have confirmed that physical expressions are a determining factor in the quality of the solutions in a spatial navigational problem. Moreover, it was found that the combination of foot gestures with visual feedback resulted in the best task performance (p\u3c .001). Results have also shown that embodied interaction-based multimodal interface decreased execution errors that occurred in the cyber-physical scenarios (p \u3c .001). Therefore we conclude that appropriate use of interaction and feedback modalities allows the operators maintain their focus of attention, reduce errors, and enhance task performance in solving the decision making problems
An analysis of the application of AI to the development of intelligent aids for flight crew tasks
This report presents the results of a study aimed at developing a basis for applying artificial intelligence to the flight deck environment of commercial transport aircraft. In particular, the study was comprised of four tasks: (1) analysis of flight crew tasks, (2) survey of the state-of-the-art of relevant artificial intelligence areas, (3) identification of human factors issues relevant to intelligent cockpit aids, and (4) identification of artificial intelligence areas requiring further research
Recent Progress in Image Deblurring
This paper comprehensively reviews the recent development of image
deblurring, including non-blind/blind, spatially invariant/variant deblurring
techniques. Indeed, these techniques share the same objective of inferring a
latent sharp image from one or several corresponding blurry images, while the
blind deblurring techniques are also required to derive an accurate blur
kernel. Considering the critical role of image restoration in modern imaging
systems to provide high-quality images under complex environments such as
motion, undesirable lighting conditions, and imperfect system components, image
deblurring has attracted growing attention in recent years. From the viewpoint
of how to handle the ill-posedness which is a crucial issue in deblurring
tasks, existing methods can be grouped into five categories: Bayesian inference
framework, variational methods, sparse representation-based methods,
homography-based modeling, and region-based methods. In spite of achieving a
certain level of development, image deblurring, especially the blind case, is
limited in its success by complex application conditions which make the blur
kernel hard to obtain and be spatially variant. We provide a holistic
understanding and deep insight into image deblurring in this review. An
analysis of the empirical evidence for representative methods, practical
issues, as well as a discussion of promising future directions are also
presented.Comment: 53 pages, 17 figure
Recommended from our members
Scoping a vision for formative e-assessment: a project report for JISC
Assessment is an integral part of teaching and learning. If the relationship between teaching and learning were causal, i. e. if students always mastered the intended learning outcomes of a particular sequence of instruction, assessment would be superfluous. Experience and research suggest this is not the case: what is learnt can often be quite different from what is taught. Formative assessment is motivated by a concern with the elicitation of relevant information about student understanding and / or achievement, its interpretation and an exploration of how it can lead to actions that result in better learning. In the context of a policy drive towards technology-enhanced approaches to teaching and learning, the question of the role of digital technologies is key and it is the latter on which this project particularly focuses. The project and its deliverables have been informed by recent and relevant literature, in particular recent work by Black andIn this work, they put forward a framework which suggests that assessment for learning their term for formative assessment can be conceptualised as consisting of a number of aspects and five keystrategies. The key aspects revolve around the where the learner is going, where the learner is right now and how she can get there and examines the role played by the teacher, peers and the learner. Language: English Keywords: assessments, case studies, design patterns, e-assessmen
- …