Monthly Archives: June 2017

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On the limitations of cognitive control from the human psychological perspective

June 20, 2017 12:44 ,

Tarek Amer, Karen L. Campbell, Lynn Hasher, Cognitive Control As a Double-Edged Sword, Trends in Cognitive Sciences, Volume 20, Issue 12, 2016, Pages 905-915, ISSN 1364-6613, DOI: 10.1016/j.tics.2016.10.002.

Cognitive control, the ability to limit attention to goal-relevant information, aids performance on a wide range of laboratory tasks. However, there are many day-to-day functions which require little to no control and others which even benefit from reduced control. We review behavioral and neuroimaging evidence demonstrating that reduced control can enhance the performance of both older and, under some circumstances, younger adults. Using healthy aging as a model, we demonstrate that decreased cognitive control benefits performance on tasks ranging from acquiring and using environmental information to generating creative solutions to problems. Cognitive control is thus a double-edged sword – aiding performance on some tasks when fully engaged, and many others when less engaged.

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

A proposal that explains why the human brain seems bayesian but finds difficulties in using probabilities: because it uses sampling

June 20, 2017 11:26 ,

Adam N. Sanborn, Nick Chater, Bayesian Brains without Probabilities, Trends in Cognitive Sciences, Volume 20, Issue 12, 2016, Pages 883-893, ISSN 1364-6613, DOI: 10.1016/j.tics.2016.10.003.

Bayesian explanations have swept through cognitive science over the past two decades, from intuitive physics and causal learning, to perception, motor control and language. Yet people flounder with even the simplest probability questions. What explains this apparent paradox? How can a supposedly Bayesian brain reason so poorly with probabilities? In this paper, we propose a direct and perhaps unexpected answer: that Bayesian brains need not represent or calculate probabilities at all and are, indeed, poorly adapted to do so. Instead, the brain is a Bayesian sampler. Only with infinite samples does a Bayesian sampler conform to the laws of probability; with finite samples it systematically generates classic probabilistic reasoning errors, including the unpacking effect, base-rate neglect, and the conjunction fallacy.

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

State of the art of symbolic planning, particularly the one that optimizes some cost, and a novel approach

June 20, 2017 11:22 ,

Álvaro Torralba, Vidal Alcázar, Peter Kissmann, Stefan Edelkamp, Efficient symbolic search for cost-optimal planning, Artificial Intelligence, Volume 242, January 2017, Pages 52-79, ISSN 0004-3702, DOI: 10.1016/j.artint.2016.10.001.

In cost-optimal planning we aim to find a sequence of operators that achieve a set of goals with minimum cost. Symbolic search with Binary Decision Diagrams (BDDs) performs efficient state space exploration in terms of time and memory. This is crucial in optimal settings, in which large parts of the state space must be explored in order to prove optimality. However, the development of accurate heuristics for explicit-state search in recent years have left symbolic search techniques in a secondary place. In this article we propose two orthogonal improvements for symbolic search planning. On the one hand, we analyze and compare different methods for image computation in order to efficiently perform the successor generation on symbolic search. Image computation is the main bottleneck of symbolic search algorithms so an efficient computation is paramount for efficient symbolic search planning. On the other hand, we study how to use state-invariant constraints to prune states in symbolic search. This is essential in regression search but it is yet to be exploited in symbolic search planners. Experiments with symbolic bidirectional uniform-cost search and symbolic A ⁎ search with PDBs show remarkable performance improvements on most IPC benchmark domains. Overall, with the help of our improvements, symbolic bidirectional search outperforms explicit-state search with state-of-the-art heuristics such as LM-cut across many different domains.

Posted in: Artificial Intelligence , Tagged:

Model-based reinforcement learning with a reduced number of basis functions to aproximate the value function, a study of its convergence guarantees, and a nice state of the art on the use of (mdel-based) reinforcement learning for automatic control

June 20, 2017 11:05 ,

Rushikesh Kamalapurkar, Joel A. Rosenfeld, Warren E. Dixon, Efficient model-based reinforcement learning for approximate online optimal control, Automatica, Volume 74, 2016, Pages 247-258, ISSN 0005-1098, DOI: 10.1016/j.automatica.2016.08.004.

An infinite horizon optimal regulation problem is solved online for a deterministic control-affine nonlinear dynamical system using a state following (StaF) kernel method to approximate the value function. Unlike traditional methods that aim to approximate a function over a large compact set, the StaF kernel method aims to approximate a function in a small neighborhood of a state that travels within a compact set. Simulation results demonstrate that stability and approximate optimality of the control system can be achieved with significantly fewer basis functions than may be required for global approximation methods.

Posted in: Applications of reinforcement learning to control engineering , Tagged: ,

A novel particle filter algorithm with an adaptive number of particles, and a curious and interesting table I about the pros and cons of different sensors

June 20, 2017 10:46 ,

T. de J. Mateo Sanguino and F. Ponce Gómez, “Toward Simple Strategy for Optimal Tracking and Localization of Robots With Adaptive Particle Filtering,” in IEEE/ASME Transactions on Mechatronics, vol. 21, no. 6, pp. 2793-2804, Dec. 2016.DOI: 10.1109/TMECH.2016.2531629.

The ability of robotic systems to autonomously understand and/or navigate in uncertain environments is critically dependent on fairly accurate strategies, which are not always optimally achieved due to effectiveness, computational cost, and parameter settings. In this paper, we propose a novel and simple adaptive strategy to increase the efficiency and drastically reduce the computational effort in particle filters (PFs). The purpose of the adaptive approach (dispersion-based adaptive particle filter – DAPF) is to provide higher number of particles during the initial searching state (when the localization presents greater uncertainty) and fewer particles during the subsequent state (when the localization exhibits less uncertainty). With the aim of studying the dynamical PF behavior regarding others and putting the proposed algorithm into practice, we designed a methodology based on different target applications and a Kinect sensor. The various experiments conducted for both color tracking and mobile robot localization problems served to demonstrate that the DAPF algorithm can be further generalized. As a result, the DAPF approach significantly improved the computational performance over two well-known filtering strategies: 1) the classical PF with fixed particle set sizes, and 2) the adaptive technique named Kullback-Leiber distance.

Posted in: Mobile Robot Localization , Tagged: ,

How hierarchical reinforcement learning resembles human creativity, i.e., matching the psychological aspects with the engineering ones

June 20, 2017 10:26 ,

Thomas R. Colin, Tony Belpaeme, Angelo Cangelosi, Nikolas Hemion, Hierarchical reinforcement learning as creative problem solving, Robotics and Autonomous Systems, Volume 86, 2016, Pages 196-206, ISSN 0921-8890, DOI: 10.1016/j.robot.2016.08.021.

Although creativity is studied from philosophy to cognitive robotics, a definition has proven elusive. We argue for emphasizing the creative process (the cognition of the creative agent), rather than the creative product (the artifact or behavior). Owing to developments in experimental psychology, the process approach has become an increasingly attractive way of characterizing creative problem solving. In particular, the phenomenon of insight, in which an individual arrives at a solution through a sudden change in perspective, is a crucial component of the process of creativity. These developments resonate with advances in machine learning, in particular hierarchical and modular approaches, as the field of artificial intelligence aims for general solutions to problems that typically rely on creativity in humans or other animals. We draw a parallel between the properties of insight according to psychology and the properties of Hierarchical Reinforcement Learning (HRL) systems for embodied agents. Using the Creative Systems Framework developed by Wiggins and Ritchie, we analyze both insight and HRL, establishing that they are creative in similar ways. We highlight the key challenges to be met in order to call an artificial system “insightful”.

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

Survey and taxonomy of path planning algorithms

June 20, 2017 10:21 ,

Thi Thoa Mac, Cosmin Copot, Duc Trung Tran, Robin De Keyser, Heuristic approaches in robot path planning: A survey, Robotics and Autonomous Systems, Volume 86, 2016, Pages 13-28, ISSN 0921-8890, DOI: 10.1016/j.robot.2016.08.001.

Autonomous navigation of a robot is a promising research domain due to its extensive applications. The navigation consists of four essential requirements known as perception, localization, cognition and path planning, and motion control in which path planning is the most important and interesting part. The proposed path planning techniques are classified into two main categories: classical methods and heuristic methods. The classical methods consist of cell decomposition, potential field method, subgoal network and road map. The approaches are simple; however, they commonly consume expensive computation and may possibly fail when the robot confronts with uncertainty. This survey concentrates on heuristic-based algorithms in robot path planning which are comprised of neural network, fuzzy logic, nature-inspired algorithms and hybrid algorithms. In addition, potential field method is also considered due to the good results. The strengths and drawbacks of each algorithm are discussed and future outline is provided.

Posted in: Robot motion planning , Tagged: , ,

Interesting mixture of automated planning with reinforcement learning

June 19, 2017 12:25 ,

Matteo Leonetti, Luca Iocchi, Peter Stone, A synthesis of automated planning and reinforcement learning for efficient, robust decision-making, Artificial Intelligence, Volume 241, 2016, Pages 103-130, ISSN 0004-3702, DOI: 10.1016/j.artint.2016.07.004.

Automated planning and reinforcement learning are characterized by complementary views on decision making: the former relies on previous knowledge and computation, while the latter on interaction with the world, and experience. Planning allows robots to carry out different tasks in the same domain, without the need to acquire knowledge about each one of them, but relies strongly on the accuracy of the model. Reinforcement learning, on the other hand, does not require previous knowledge, and allows robots to robustly adapt to the environment, but often necessitates an infeasible amount of experience. We present Domain Approximation for Reinforcement LearnING (DARLING), a method that takes advantage of planning to constrain the behavior of the agent to reasonable choices, and of reinforcement learning to adapt to the environment, and increase the reliability of the decision making process. We demonstrate the effectiveness of the proposed method on a service robot, carrying out a variety of tasks in an office building. We find that when the robot makes decisions by planning alone on a given model it often fails, and when it makes decisions by reinforcement learning alone it often cannot complete its tasks in a reasonable amount of time. When employing DARLING, even when seeded with the same model that was used for planning alone, however, the robot can quickly learn a behavior to carry out all the tasks, improves over time, and adapts to the environment as it changes.

Posted in: Artificial Intelligence , Tagged: ,

Reducing error in time synchronization for multisensor arrangements in aerial applications, with interesting formulae for the clock drift of IMUs

June 19, 2017 12:18 ,

J. Li, L. Jia and G. Liu, “Multisensor Time Synchronization Error Modeling and Compensation Method for Distributed POS,” in IEEE Transactions on Instrumentation and Measurement, vol. 65, no. 11, pp. 2637-2645, Nov. 2016. DOI: 10.1109/TIM.2016.2598020.

An airborne distributed position and orientation system (POS) is high-precision measurement equipment that can accurately provide multinode time-spatial reference for novel remote sensing system as multitask imaging sensors and array antenna synthetic aperture radar. However, it is difficult for multisensor to precisely acquire information at the same moment and result in data fusion error. Thus, the measurement precision is severely degraded. To solve the problem, a multisensor time synchronization error modeling and compensation method is proposed. Based on the component and operation principles of distributed POS, the time synchronization mechanism is analyzed. Multisensor time synchronization error models that include time delay error, random error, and time-varying error are established. A time synchronization error compensation method of the distributed POS is proposed. The experiment results show that the proposed method can accurately calibrate and compensate for the time synchronization error, and improve the measurement precision of the distributed POS. It verified the validity of the proposed method.

Posted in: Real-Time Systems , Tagged:

Globally optimal ICP

June 19, 2017 11:55 ,

J. Yang, H. Li, D. Campbell and Y. Jia, “Go-ICP: A Globally Optimal Solution to 3D ICP Point-Set Registration,” in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 38, no. 11, pp. 2241-2254, Nov. 1 2016. DOI: 10.1109/TPAMI.2015.2513405.

The Iterative Closest Point (ICP) algorithm is one of the most widely used methods for point-set registration. However, being based on local iterative optimization, ICP is known to be susceptible to local minima. Its performance critically relies on the quality of the initialization and only local optimality is guaranteed. This paper presents the first globally optimal algorithm, named Go-ICP, for Euclidean (rigid) registration of two 3D point-sets under the $L_2$ error metric defined in ICP. The Go-ICP method is based on a branch-and-bound scheme that searches the entire 3D motion space $SE(3)$ . By exploiting the special structure of $SE(3)$ geometry, we derive novel upper and lower bounds for the registration error function. Local ICP is integrated into the BnB scheme, which speeds up the new method while guaranteeing global optimality. We also discuss extensions, addressing the issue of outlier robustness. The evaluation demonstrates that the proposed method is able to produce reliable registration results regardless of the initialization. Go-ICP can be applied in scenarios where an optimal solution is desirable or where a good initialization is not always available.

Posted in: Mobile Robot Localization , Tagged: ,