Category Archives: Cognitive Sciences

Survey of Hierarchical Task Planning

March 11, 2015 15:28 ,

Ilche Georgievski, Marco Aiello, 2015, HTN planning: Overview, comparison, and beyond, Artificial Intelligence, Volume 222, May 2015, Pages 124-156, ISSN 0004-3702, DOI: 10.1016/j.artint.2015.02.002.

Hierarchies are one of the most common structures used to understand and conceptualise the world. Within the field of Artificial Intelligence (AI) planning, which deals with the automation of world-relevant problems, Hierarchical Task Network (HTN) planning is the branch that represents and handles hierarchies. In particular, the requirement for rich domain knowledge to characterise the world enables HTN planning to be very useful, and also to perform well. However, the history of almost 40 years obfuscates the current understanding of HTN planning in terms of accomplishments, planning models, similarities and differences among hierarchical planners, and its current and objective image. On top of these issues, the ability of hierarchical planning to truly cope with the requirements of real-world applications has been often questioned. As a remedy, we propose a framework-based approach where we first provide a basis for defining different formal models of hierarchical planning, and define two models that comprise a large portion of HTN planners. Second, we provide a set of concepts that helps in interpreting HTN planners from the aspect of their search space. Then, we analyse and compare the planners based on a variety of properties organised in five segments, namely domain authoring, expressiveness, competence, computation and applicability. Furthermore, we select Web service composition as a real-world and current application, and classify and compare the approaches that employ HTN planning to solve the problem of service composition. Finally, we conclude with our findings and present directions for future work. In summary, we provide a novel and comprehensive viewpoint on a core AI planning technique.

Posted in: Artificial Intelligence , Tagged:

On the process of the brain for detecting similarities, with a proposal for its structure and its timing

March 11, 2015 15:20 ,

Qingfei Chen, Xiuling Liang, Peng Li, Chun Ye, Fuhong Li, Yi Lei, Hong Li, 2015, The processing of perceptual similarity with different features or spatial relations as revealed by P2/P300 amplitude, International Journal of Psychophysiology, Volume 95, Issue 3, March 2015, Pages 379-387, ISSN 0167-8760, DOI: 10.1016/j.ijpsycho.2015.01.009.

Visual features such as “color” and spatial relations such as “above” or “beside” have complex effects on similarity and difference judgments. We examined the relative impact of features and spatial relations on similarity and difference judgments via ERPs in an S1–S2 paradigm. Subjects were required to compare a remembered geometric shape (S1) with a second one (S2), and made a “high” or “low” judgment of either similarity or difference in separate blocks of trials. We found three main differences that suggest that the processing of features and spatial relations engages distinct neural processes. The first difference is a P2 effect in fronto-central regions which is sensitive to the presence of a feature difference. The second difference is a P300 in centro-parietal regions that is larger for difference judgments than for similarity judgments. Finally, the P300 effect elicited by feature differences was larger relative to spatial relation differences. These results supported the view that similarity judgments involve structural alignment rather than simple feature and relation matches, and furthermore, indicate the similarity judgment could be divided into three phases: feature or relation comparison (P2), structural alignment (P3 at 300–400 ms), and categorization (P3 at 450–550 ms).

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

On the role of emotions in cognition, in particular in cognitive control

March 11, 2015 15:14 ,

Michael Inzlicht, Bruce D. Bartholow, Jacob B. Hirsh, 2015, Emotional foundations of cognitive control, Trends in Cognitive Sciences, Volume 19, Issue 3, March 2015, Pages 126-132, DOI: 10.1016/j.tics.2015.01.004.

Often seen as the paragon of higher cognition, here we suggest that cognitive control is dependent on emotion. Rather than asking whether control is influenced by emotion, we ask whether control itself can be understood as an emotional process. Reviewing converging evidence from cybernetics, animal research, cognitive neuroscience, and social and personality psychology, we suggest that cognitive control is initiated when goal conflicts evoke phasic changes to emotional primitives that both focus attention on the presence of goal conflicts and energize conflict resolution to support goal-directed behavior. Critically, we propose that emotion is not an inert byproduct of conflict but is instrumental in recruiting control. Appreciating the emotional foundations of control leads to testable predictions that can spur future research.

Posted in: Cognitive sciences , Tagged: ,

On the not-so-domain-generic nature of statistical learning in the human brain

March 11, 2015 15:09 ,

Ram Frost, Blair C. Armstrong, Noam Siegelman, Morten H. Christiansen, 2015, Domain generality versus modality specificity: the paradox of statistical learning, Trends in Cognitive Sciences, Volume 19, Issue 3, March 2015, Pages 117-125, DOI: 10.1016/j.tics.2014.12.010.

Statistical learning (SL) is typically considered to be a domain-general mechanism by which cognitive systems discover the underlying distributional properties of the input. However, recent studies examining whether there are commonalities in the learning of distributional information across different domains or modalities consistently reveal modality and stimulus specificity. Therefore, important questions are how and why a hypothesized domain-general learning mechanism systematically produces such effects. Here, we offer a theoretical framework according to which SL is not a unitary mechanism, but a set of domain-general computational principles that operate in different modalities and, therefore, are subject to the specific constraints characteristic of their respective brain regions. This framework offers testable predictions and we discuss its computational and neurobiological plausibility.

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

Abstracting and representing tasks performed under Learning from Demonstration, using bayesian non-parametric time-series analysis (good review of both LfD and HMMs for time-series)

February 10, 2015 16:37 ,

Scott Niekum, Sarah Osentoski, George Konidaris, Sachin Chitta, Bhaskara Marthi, Andrew G. Barto (2015), Learning grounded finite-state representations from unstructured demonstrations, The International Journal of Robotics Research, vol. 34, pp. 131-157. DOI: 10.1177/0278364914554471

Robots exhibit flexible behavior largely in proportion to their degree of knowledge about the world. Such knowledge is often meticulously hand-coded for a narrow class of tasks, limiting the scope of possible robot competencies. Thus, the primary limiting factor of robot capabilities is often not the physical attributes of the robot, but the limited time and skill of expert programmers. One way to deal with the vast number of situations and environments that robots face outside the laboratory is to provide users with simple methods for programming robots that do not require the skill of an expert. For this reason, learning from demonstration (LfD) has become a popular alternative to traditional robot programming methods, aiming to provide a natural mechanism for quickly teaching robots. By simply showing a robot how to perform a task, users can easily demonstrate new tasks as needed, without any special knowledge about the robot. Unfortunately, LfD often yields little knowledge about the world, and thus lacks robust generalization capabilities, especially for complex, multi-step tasks. We present a series of algorithms that draw from recent advances in Bayesian non-parametric statistics and control theory to automatically detect and leverage repeated structure at multiple levels of abstraction in demonstration data. The discovery of repeated structure provides critical insights into task invariants, features of importance, high-level task structure, and appropriate skills for the task. This culminates in the discovery of a finite-state representation of the task, composed of grounded skills that are flexible and reusable, providing robust generalization and transfer in complex, multi-step robotic tasks. These algorithms are tested and evaluated using a PR2 mobile manipulator, showing success on several complex real-world tasks, such as furniture assembly.

Posted in: Learning from Demonstration , Tagged: , ,

Scientific limitations to the non-scientific idea that super-intelligence will come (for exterminating humans)

January 23, 2015 20:00 ,

Ernest Davis, Ethical guidelines for a superintelligence, Artificial Intelligence, Volume 220, March 2015, Pages 121-124, ISSN 0004-3702, DOI: 10.1016/j.artint.2014.12.003.

Nick Bostrom, in his new book SuperIntelligence, argues that the creation of an artificial intelligence with human-level intelligence will be followed fairly soon by the existence of an almost omnipotent superintelligence, with consequences that may well be disastrous for humanity. He considers that it is therefore a top priority for mankind to figure out how to imbue such a superintelligence with a sense of morality; however, he considers that this task is very difficult. I discuss a number of flaws in his analysis, particularly the viewpoint that implementing ethical behavior is an especially difficult problem in AI research.

Posted in: Artificial Intelligence , Tagged: ,

How to bypass the NP-hardness of estimating the best explanation of given data (instantiated as MAP, i.e., Maximum A Posteriori, not as maximum likelihood) in discrete Bayesian Networks, through distinction of relevant and irrelevant variables

January 23, 2015 17:15 ,

Johan Kwisthout, Most frugal explanations in Bayesian networks, Artificial Intelligence, Volume 218, January 2015, Pages 56-73, ISSN 0004-3702, DOI: 10.1016/j.artint.2014.10.001

Inferring the most probable explanation to a set of variables, given a partial observation of the remaining variables, is one of the canonical computational problems in Bayesian networks, with widespread applications in AI and beyond. This problem, known as MAP, is computationally intractable (NP-hard) and remains so even when only an approximate solution is sought. We propose a heuristic formulation of the MAP problem, denoted as Inference to the Most Frugal Explanation (MFE), based on the observation that many intermediate variables (that are neither observed nor to be explained) are irrelevant with respect to the outcome of the explanatory process. An explanation based on few samples (often even a singleton sample) from these irrelevant variables is typically almost as good as an explanation based on (the computationally costly) marginalization over these variables. We show that while MFE is computationally intractable in general (as is MAP), it can be tractably approximated under plausible situational constraints, and its inferences are fairly robust with respect to which intermediate variables are considered to be relevant.

Posted in: Artificial Intelligence , Tagged: , ,

On search as a consequence of the exploration-exploitation trade-off, and as a core element in human cognition

January 23, 2015 16:58 ,

Thomas T. Hills, Peter M. Todd, David Lazer, A. David Redish, Iain D. Couzin, the Cognitive Search Research Group, Exploration versus exploitation in space, mind, and society, Trends in Cognitive Sciences, Volume 19, Issue 1, January 2015, Pages 46-54, ISSN 1364-6613, DOI: 10.1016/j.tics.2014.10.004.

Search is a ubiquitous property of life. Although diverse domains have worked on search problems largely in isolation, recent trends across disciplines indicate that the formal properties of these problems share similar structures and, often, similar solutions. Moreover, internal search (e.g., memory search) shows similar characteristics to external search (e.g., spatial foraging), including shared neural mechanisms consistent with a common evolutionary origin across species. Search problems and their solutions also scale from individuals to societies, underlying and constraining problem solving, memory, information search, and scientific and cultural innovation. In summary, search represents a core feature of cognition, with a vast influence on its evolution and processes across contexts and requiring input from multiple domains to understand its implications and scope.

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

On the way humans reduce perceptual information during decision making, falling apart from statistically optimal behavior, in order to deal with the overwhelming sensory flow

January 23, 2015 16:52 ,

Christopher Summerfield, Konstantinos Tsetsos, Do humans make good decisions?, Trends in Cognitive Sciences, Volume 19, Issue 1, January 2015, Pages 27-34, ISSN 1364-6613, DOI: 10.1016/j.tics.2014.11.005

Human performance on perceptual classification tasks approaches that of an ideal observer, but economic decisions are often inconsistent and intransitive, with preferences reversing according to the local context. We discuss the view that suboptimal choices may result from the efficient coding of decision-relevant information, a strategy that allows expected inputs to be processed with higher gain than unexpected inputs. Efficient coding leads to \u2018robust\u2019 decisions that depart from optimality but maximise the information transmitted by a limited-capacity system in a rapidly-changing world. We review recent work showing that when perceptual environments are variable or volatile, perceptual decisions exhibit the same suboptimal context-dependence as economic choices, and we propose a general computational framework that accounts for findings across the two domains.

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