Category Archives: Cognitive Sciences

A good intro about actor-critic and decision making without model on MDPs

June 30, 2017 11:31 ,

J. Wang and I. C. Paschalidis, “An Actor-Critic Algorithm With Second-Order Actor and Critic,” in IEEE Transactions on Automatic Control, vol. 62, no. 6, pp. 2689-2703, June 2017.DOI: 10.1109/TAC.2016.2616384.

Actor-critic algorithms solve dynamic decision making problems by optimizing a performance metric of interest over a user-specified parametric class of policies. They employ a combination of an actor, making policy improvement steps, and a critic, computing policy improvement directions. Many existing algorithms use a steepest ascent method to improve the policy, which is known to suffer from slow convergence for ill-conditioned problems. In this paper, we first develop an estimate of the (Hessian) matrix containing the second derivatives of the performance metric with respect to policy parameters. Using this estimate, we introduce a new second-order policy improvement method and couple it with a critic using a second-order learning method. We establish almost sure convergence of the new method to a neighborhood of a policy parameter stationary point. We compare the new algorithm with some existing algorithms in two applications and demonstrate that it leads to significantly faster convergence.

Posted in: Artificial Intelligence , Tagged: , ,

Evidences that the human brain has quantifying properties -i.e., ability to discriminate between sets of different sizes- as a result of evolution, but that numerical cognition is a result of culture

June 30, 2017 11:05 ,

Rafael E. Núñez, Is There Really an Evolved Capacity for Number?, Trends in Cognitive Sciences, Volume 21, Issue 6, June 2017, Pages 409-424, ISSN 1364-6613, DOI: 10.1016/j.tics.2017.03.005.

Humans and other species have biologically endowed abilities for discriminating quantities. A widely accepted view sees such abilities as an evolved capacity specific for number and arithmetic. This view, however, is based on an implicit teleological rationale, builds on inaccurate conceptions of biological evolution, downplays human data from non-industrialized cultures, overinterprets results from trained animals, and is enabled by loose terminology that facilitates teleological argumentation. A distinction between quantical (e.g., quantity discrimination) and numerical (exact, symbolic) cognition is needed: quantical cognition provides biologically evolved preconditions for numerical cognition but it does not scale up to number and arithmetic, which require cultural mediation. The argument has implications for debates about the origins of other special capacities – geometry, music, art, and language.

Posted in: Psycho-physiological bases of engineering , Tagged:

Reinforcement learning to learn the model of the world intrinsically motivated

June 27, 2017 12:10 ,

Todd Hester, Peter Stone, Intrinsically motivated model learning for developing curious robots, Artificial Intelligence, Volume 247, June 2017, Pages 170-186, ISSN 0004-3702, DOI: 10.1016/j.artint.2015.05.002.

Reinforcement Learning (RL) agents are typically deployed to learn a specific, concrete task based on a pre-defined reward function. However, in some cases an agent may be able to gain experience in the domain prior to being given a task. In such cases, intrinsic motivation can be used to enable the agent to learn a useful model of the environment that is likely to help it learn its eventual tasks more efficiently. This paradigm fits robots particularly well, as they need to learn about their own dynamics and affordances which can be applied to many different tasks. This article presents the texplore with Variance-And-Novelty-Intrinsic-Rewards algorithm (texplore-vanir), an intrinsically motivated model-based RL algorithm. The algorithm learns models of the transition dynamics of a domain using random forests. It calculates two different intrinsic motivations from this model: one to explore where the model is uncertain, and one to acquire novel experiences that the model has not yet been trained on. This article presents experiments demonstrating that the combination of these two intrinsic rewards enables the algorithm to learn an accurate model of a domain with no external rewards and that the learned model can be used afterward to perform tasks in the domain. While learning the model, the agent explores the domain in a developing and curious way, progressively learning more complex skills. In addition, the experiments show that combining the agent’s intrinsic rewards with external task rewards enables the agent to learn faster than using external rewards alone. We also present results demonstrating the applicability of this approach to learning on robots.

Posted in: Applications of reinforcement learning to robots, Artificial Intelligence , Tagged: ,

State of the art and historical background of the classical divergence between AI and robotics

June 27, 2017 12:03 ,

Kanna Rajan, Alessandro Saffiotti, Towards a science of integrated AI and Robotics, Artificial Intelligence, Volume 247, June 2017, Pages 1-9, ISSN 0004-3702, DOI: 10.1016/j.artint.2017.03.003.

The early promise of the impact of machine intelligence did not involve the partitioning of the nascent field of Artificial Intelligence. The founders of AI envisioned the notion of embedded intelligence as being conjoined between perception, reasoning and actuation. Yet over the years the fields of AI and Robotics drifted apart. Practitioners of AI focused on problems and algorithms abstracted from the real world. Roboticists, generally with a background in mechanical and electrical engineering, concentrated on sensori-motor functions. That divergence is slowly being bridged with the maturity of both fields and with the growing interest in autonomous systems. This special issue brings together the state of the art and practice of the emergent field of integrated AI and Robotics, and highlights the key areas along which this current evolution of machine intelligence is heading.

Posted in: Artificial Intelligence, Robotics , Tagged:

Modelling hierarchical stochastic signals (i.e., decomposable into sub-signals hierarchichally)

June 27, 2017 11:17 ,

Truyen Tran, Dinh Phung, Hung Bui, Svetha Venkatesh, Hierarchical semi-Markov conditional random fields for deep recursive sequential data, Artificial Intelligence, Volume 246, May 2017, Pages 53-85, ISSN 0004-3702, DOI: 10.1016/j.artint.2017.02.003.

We present the hierarchical semi-Markov conditional random field (HSCRF), a generalisation of linear-chain conditional random fields to model deep nested Markov processes. It is parameterised as a conditional log-linear model and has polynomial time algorithms for learning and inference. We derive algorithms for partially-supervised learning and constrained inference. We develop numerical scaling procedures that handle the overflow problem. We show that when depth is two, the HSCRF can be reduced to the semi-Markov conditional random fields. Finally, we demonstrate the HSCRF on two applications: (i) recognising human activities of daily living (ADLs) from indoor surveillance cameras, and (ii) noun-phrase chunking. The HSCRF is capable of learning rich hierarchical models with reasonable accuracy in both fully and partially observed data cases.

Posted in: Artificial Intelligence, Probability and statistics , Tagged: , , ,

A summary of the Clarion cognitive architecture

June 27, 2017 11:07 ,

Ron Sun, Anatomy of the Mind: a Quick Overview, Cognitive Computation, February 2017, Volume 9, Issue 1, pp 1–4, DOI: 10.1007/s12559-016-9444-2.

The recently published book, “Anatomy of the Mind,” explains psychological (cognitive) mechanisms, processes, and functionalities through a comprehensive computational theory of the human mind—that is, a cognitive architecture. The goal of the work has been to develop a unified framework and then to develop process-based mechanistic understanding of psychological phenomena within the unified framework. In this article, I will provide a quick overview of the work.

Posted in: Cognitive sciences, Psycho-physiological bases of engineering , Tagged: ,

How to improve statistical results obtained from limited set-ups through active sampling, and a nice review of possible pitfalls in conducting statistical research (and a mention to “pre-registration” of hypothesis and plans to be peer-reviewed before submitting the results)

June 27, 2017 08:42 ,

Romy Lorenz, Adam Hampshire, Robert Leech, Neuroadaptive Bayesian Optimization and Hypothesis Testing, Trends in Cognitive Sciences, Volume 21, Issue 3, March 2017, Pages 155-167, ISSN 1364-6613, DOI: 10.1016/j.tics.2017.01.006.

Cognitive neuroscientists are often interested in broad research questions, yet use overly narrow experimental designs by considering only a small subset of possible experimental conditions. This limits the generalizability and reproducibility of many research findings. Here, we propose an alternative approach that resolves these problems by taking advantage of recent developments in real-time data analysis and machine learning. Neuroadaptive Bayesian optimization is a powerful strategy to efficiently explore more experimental conditions than is currently possible with standard methodology. We argue that such an approach could broaden the hypotheses considered in cognitive science, improving the generalizability of findings. In addition, Bayesian optimization can be combined with preregistration to cover exploration, mitigating researcher bias more broadly and improving reproducibility.

Posted in: Cognitive sciences, Probability and statistics , Tagged: ,

Approach to explain gaze: gaze is directed to task- and goal-relevant scene regions

June 21, 2017 12:12 ,

John M. Henderson, Gaze Control as Prediction, Trends in Cognitive Sciences, Volume 21, Issue 1, January 2017, Pages 15-23, ISSN 1364-6613, DOI: 10.1016/j.tics.2016.11.003.

The recent study of overt attention during complex scene viewing has emphasized explaining gaze behavior in terms of image properties and image salience independently of the viewer’s intentions and understanding of the scene. In this Opinion article, I outline an alternative approach proposing that gaze control in natural scenes can be characterized as the result of knowledge-driven prediction. This view provides a theoretical context for integrating and unifying many of the disparate phenomena observed in active scene viewing, offers the potential for integrating the behavioral study of gaze with the neurobiological study of eye movements, and provides a theoretical framework for bridging gaze control and other related areas of perception and cognition at both computational and neurobiological levels of analysis.

Posted in: Psycho-physiological bases of engineering , Tagged:

Demonstration that a theory of cortical function (“predictive coding”) can perform bayesian inference in some tasks, with a nice related work of physiological foundations of probability distribution representation in neurons and of bayesian inference

June 21, 2017 11:57 ,

M. W. Spratling, A neural implementation of Bayesian inference based on predictive coding, Connection Science, Volume 28, 2016 – Issue 4, DOI: 10.1080/09540091.2016.1243655.

Predictive coding (PC) is a leading theory of cortical function that has previously been shown to explain a great deal of neurophysiological and psychophysical data. Here it is shown that PC can perform almost exact Bayesian inference when applied to computing with population codes. It is demonstrated that the proposed algorithm, based on PC, can: decode probability distributions encoded as noisy population codes; combine priors with likelihoods to calculate posteriors; perform cue integration and cue segregation; perform function approximation; be extended to perform hierarchical inference; simultaneously represent and reason about multiple stimuli; and perform inference with multi-modal and non-Gaussian probability distributions. PC thus provides a neural network-based method for performing probabilistic computation and provides a simple, yet comprehensive, theory of how the cerebral cortex performs Bayesian inference.

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Subgraph matching (isomorphism) using GPUs for managing commonsense knowledge, and a short list of other graph problems that have had benefit from multiprocessing

June 21, 2017 10:48 ,

Ha-Nguyen Tran, Erik Cambria, Amir Hussain, Towards GPU-Based Common-Sense Reasoning: Using Fast Subgraph Matching, Cognitive Computation, December 2016, Volume 8, Issue 6, pp 1074–1086, DOI: 10.1007/s12559-016-9418-4.

Common-sense reasoning is concerned with simulating cognitive human ability to make presumptions about the type and essence of ordinary situations encountered every day. The most popular way to represent common-sense knowledge is in the form of a semantic graph. Such type of knowledge, however, is known to be rather extensive: the more concepts added in the graph, the harder and slower it becomes to apply standard graph mining techniques.In this work, we propose a new fast subgraph matching approach to overcome these issues. Subgraph matching is the task of finding all matches of a query graph in a large data graph, which is known to be a non-deterministic polynomial time-complete problem. Many algorithms have been previously proposed to solve this problem using central processing units. Here, we present a new graphics processing unit-friendly method for common-sense subgraph matching, termed GpSense, which is designed for scalable massively parallel architectures, to enable next-generation Big Data sentiment analysis and natural language processing applications.We show that GpSense outperforms state-of-the-art algorithms and efficiently answers subgraph queries on large common-sense graphs.

Posted in: Artificial Intelligence, Graph theory , Tagged: ,