Online Planner Selection with Graph Neural Networks and Adaptive Scheduling

Automated planning is one of the foundational areas of AI. Since no single planner can work well for all tasks and domains, portfolio-based techniques have become increasingly popular in recent years. In particular, deep learning emerges as a promising methodology for online planner selection. Owing to the recent development of structural graph representations of planning tasks, we propose a graph neural network (GNN) approach to selecting candidate planners. GNNs are advantageous over a straightforward alternative, the convolutional neural networks, in that they are invariant to node permutations and that they incorporate node labels for better inference. Additionally, for cost-optimal planning, we propose a two-stage adaptive scheduling method to further improve the likelihood that a given task is solved in time. The scheduler may switch at halftime to a different planner, conditioned on the observed performance of the first one. Experimental results validate the effectiveness of the proposed method against strong baselines, both deep learning and non-deep learning based. The code is available at \url{https://github.com/matenure/GNN_planner}.Comment: AAAI 2020. Code is released at https://github.com/matenure/GNN_planner. Data set is released at https://github.com/IBM/IPC-graph-dat

The role of response mechanisms in determining reaction time performance: Piéron’s Law revisited

A response mechanism takes evaluations of the importance of potential actions and selects the most suitable. Response mechanism function is a nontrivial problem that has not received the attention it deserves within cognitive psychology. In this article, we make a case for the importance of considering response mechanism function as a constraint on cognitive processes and emphasized links with the wider problem of behavioral action selection. First, we show that, contrary to previous suggestions, a well–known model of the Stroop task (Cohen, Dunbar, & McClelland, 1990) relies on the response mechanism for a key feature of its results—the interference–facilitation asymmetry. Second, we examine a variety of response mechanisms (including that in the model of Cohen et al., 1990) and show that they all follow a law analogous to Piéron's law in relating their input to reaction time. In particular, this is true of a decision mechanism not designed to explain RT data but based on a proposed solution to the general problem of action selection and grounded in the neurobiology of the vertebrate basal ganglia. Finally, we show that the dynamics of simple artificial neurons also support a Piéron–like law

The time course of routine action

Previous studies of action selection in routinized tasks have used error rates as their sole dependent measure (e.g. Reason, 1979; Schwartz et al., 1998). Consequently, conclusions about the underlying mechanisms of correct behavior are necessarily indirect. The present experiment examines the performance of normal subjects in the prototypical coffee task (Botvinick & Plaut, 2004) when carried out in a virtual environment on screen. This has the advantage of (a) constraining the possible errors more tightly than a real world environment, and (b) giving access to latencies as an additional, finer grained measure of performance. We report error data and timing of action selection at the crucial branching points for the production of routinized task sequences both with and without a secondary task. Processing branching points leads to increased latencies. The presence of the secondary task has a greater effect on latencies at branching points than at equivalent non-branching points. Furthermore, error data and latencies dissociate, suggesting that the exact timing is a valid and valuable source of information when trying to understand the processes that govern routine tasks. The results of the experiment are discussed in relation to their implication for computational accounts of routine action selection

Does the motor system need intermittent control?

Explanation of motor control is dominated by continuous neurophysiological pathways (e.g. trans-cortical, spinal) and the continuous control paradigm. Using new theoretical development, methodology and evidence, we propose intermittent control, which incorporates a serial ballistic process within the main feedback loop, provides a more general and more accurate paradigm necessary to explain attributes highly advantageous for competitive survival and performance

The Cat Is On the Mat. Or Is It a Dog? Dynamic Competition in Perceptual Decision Making

Recent neurobiological findings suggest that the brain solves simple perceptual decision-making tasks by means of a dynamic competition in which evidence is accumulated in favor of the alternatives. However, it is unclear if and how the same process applies in more complex, real-world tasks, such as the categorization of ambiguous visual scenes and what elements are considered as evidence in this case. Furthermore, dynamic decision models typically consider evidence accumulation as a passive process disregarding the role of active perception strategies. In this paper, we adopt the principles of dynamic competition and active vision for the realization of a biologically- motivated computational model, which we test in a visual catego- rization task. Moreover, our system uses predictive power of the features as the main dimension for both evidence accumulation and the guidance of active vision. Comparison of human and synthetic data in a common experimental setup suggests that the proposed model captures essential aspects of how the brain solves perceptual ambiguities in time. Our results point to the importance of the proposed principles of dynamic competi- tion, parallel specification, and selection of multiple alternatives through prediction, as well as active guidance of perceptual strategies for perceptual decision-making and the resolution of perceptual ambiguities. These principles could apply to both the simple perceptual decision problems studied in neuroscience and the more complex ones addressed by vision research.Peer reviewe

Inside the brain of an elite athlete: The neural processes that support high achievement in sports

Events like the World Championships in athletics and the Olympic Games raise the public profile of competitive sports. They may also leave us wondering what sets the competitors in these events apart from those of us who simply watch. Here we attempt to link neural and cognitive processes that have been found to be important for elite performance with computational and physiological theories inspired by much simpler laboratory tasks. In this way we hope to inspire neuroscientists to consider how their basic research might help to explain sporting skill at the highest levels of performance

Pre-stimulus influences on auditory perception arising from sensory representations and decision processes

The qualities of perception depend not only on the sensory inputs but also on the brain state before stimulus presentation. Although the collective evidence from neuroimaging studies for a relation between prestimulus state and perception is strong, the interpretation in the context of sensory computations or decision processes has remained difficult. In the auditory system, for example, previous studies have reported a wide range of effects in terms of the perceptually relevant frequency bands and state parameters (phase/power). To dissociate influences of state on earlier sensory representations and higher-level decision processes, we collected behavioral and EEG data in human participants performing two auditory discrimination tasks relying on distinct acoustic features. Using single-trial decoding, we quantified the relation between prestimulus activity, relevant sensory evidence, and choice in different task-relevant EEG components. Within auditory networks, we found that phase had no direct influence on choice, whereas power in task-specific frequency bands affected the encoding of sensory evidence. Within later-activated frontoparietal regions, theta and alpha phase had a direct influence on choice, without involving sensory evidence. These results delineate two consistent mechanisms by which prestimulus activity shapes perception. However, the timescales of the relevant neural activity depend on the specific brain regions engaged by the respective task

Processing irrelevant location information: practice and transfer effects in a Simon task.

How humans produce cognitively driven fine motor movements is a question of fundamental importance in how we interact with the world around us. For example, we are exposed to a constant stream of information and we must select the information that is most relevant by which to guide our actions. In the present study, we employed a well-known behavioral assay called the Simon task to better understand how humans are able to learn to filter out irrelevant information. We trained subjects for four days with a visual stimulus presented, alternately, in central and lateral locations. Subjects responded with one hand moving a joystick in either the left or right direction. They were instructed to ignore the irrelevant location information and respond based on color (e.g. red to the right and green to the left). On the fifth day, an additional testing session was conducted where the task changed and the subjects had to respond by shape (e.g. triangle to the right and rectangle to the left). They were instructed to ignore the color and location, and respond based solely on the task relevant shape. We found that the magnitude of the Simon effect decreases with training, however it returns in the first few trials after a break. Furthermore, task-defined associations between response direction and color did not significantly affect the Simon effect based on shape, and no significant associative learning from the specific stimulus-response features was found for the centrally located stimuli. We discuss how these results are consistent with a model involving route suppression/gating of the irrelevant location information. Much of the learning seems to be driven by subjects learning to suppress irrelevant location information, however, this seems to be an active inhibition process that requires a few trials of experience to engage