Learning When to Quit: Meta-Reasoning for Motion Planning

Anytime motion planners are widely used in robotics. However, the relationship between their solution quality and computation time is not well understood, and thus, determining when to quit planning and start execution is unclear. In this paper, we address the problem of deciding when to stop deliberation under bounded computational capacity, so called meta-reasoning, for anytime motion planning. We propose data-driven learning methods, model-based and model-free meta-reasoning, that are applicable to different environment distributions and agnostic to the choice of anytime motion planners. As a part of the framework, we design a convolutional neural network-based optimal solution predictor that predicts the optimal path length from a given 2D workspace image. We empirically evaluate the performance of the proposed methods in simulation in comparison with baselines.Comment: 8 pages, 5 figures, Submitted to IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 202

Humans decompose tasks by trading off utility and computational cost

Human behavior emerges from planning over elaborate decompositions of tasks into goals, subgoals, and low-level actions. How are these decompositions created and used? Here, we propose and evaluate a normative framework for task decomposition based on the simple idea that people decompose tasks to reduce the overall cost of planning while maintaining task performance. Analyzing 11,117 distinct graph-structured planning tasks, we find that our framework justifies several existing heuristics for task decomposition and makes predictions that can be distinguished from two alternative normative accounts. We report a behavioral study of task decomposition ($N=806$) that uses 30 randomly sampled graphs, a larger and more diverse set than that of any previous behavioral study on this topic. We find that human responses are more consistent with our framework for task decomposition than alternative normative accounts and are most consistent with a heuristic -- betweenness centrality -- that is justified by our approach. Taken together, our results provide new theoretical insight into the computational principles underlying the intelligent structuring of goal-directed behavior

Attributed Intelligence

Human beings quickly and confidently attribute more or less intelligence to one another. What is meant by intelligence when they do so? And what are the surface features of human behaviour that determine their judgements? Because the judges of success or failure in the quest for `artificial intelligence' will be human, the answers to such questions are an essential part of cognitive science. This thesis studies such questions in the context of a maze world, complex enough to require non-trivial answers, and simple enough to analyse the answers in term of decision-making algorithms. According to Theory-theory, humans comprehend the actions of themselves and of others in terms of beliefs, desires and goals, following rational principles of utility. If so, attributing intelligence may result from an evaluation the agent's efficiency -- how closely its behaviour approximates the expected rational course of action. Alternatively, attributed intelligence could result from observing outcomes: billionaires and presidents are, by definition, intelligent. I applied Bayesian models of planning under uncertainty to data from five behavioural experiments. The results show that while most humans attribute intelligence to efficiency, a minority attributes intelligence to outcome. Understanding of differences in attributed intelligence comes from a study how people plan. Most participants can optimally plan 1-5 decisions in advance. Individually they vary in sensitivity to decision value and in planning depth. Comparing planning performance and attributed intelligence shows that observers' ability to attribute intelligence depends on their ability to plan. People attribute intelligence to efficiency in proportion to their planning ability. The less skilled planners are more likely to attribute intelligence to outcome. Moreover, model-based metrics of planning performance correlate with independent measures of cognitive performance, such as the Cognitive Reflection Test and pupil size. Eyetracking analysis of spatial planning in real-time shows that participants who score highly on independent measures of cognitive ability also plan further ahead. Taken together, these results converge on a theory of attributed intelligence as an evaluation of how efficiently an agent plans, such that depends on the observer's cognitive abilities to carry out the evaluation

Cognitive fatigue in young, middle-aged, and older people: behavioral and functional neuroimaging investigations

In our modern societies, humans are constantly cognitively solicited until a relatively advanced age. This continuous cognitive stimulation can obviously be experienced at work but it can also more insidiously come from overcrowded environments, social networks, or constant advertisement on the internet, which eventually bury people in an uninterrupted flow of information. Cognitive fatigue has progressively become one of the most prevalent causes of accidents in everyday life (Dinges, 1995; Shen et al., 2008) but also in the workplace (McCormick et al., 2012). If cognitive fatigue can be considered a normal and adaptive response to long-lasting tasks (Boksem & Tops, 2008), it can also lead to tragic consequences in certain professions. For example, studies have already found evidence of attention drops in airplane pilots (Bartlett, 1943) or large speed variation in car or train drivers under cognitive fatigue (Brown, 1994; Campagne et al., 2004; Kecklund & Akerstedt, 1993; Torsvall & Akerstedt, 1987). This phenomenon is also striking in emergency services like in firefighters (Aisbett & Nichols, 2007; Aisbett et al., 2012; Ferguson et al., 2016) and in intensive care unit physicians (Maltese et al., 2016). When continuously exposed to cognitive fatigue, some individuals can unfortunately develop the so-called burnout condition (Maslach et al., 2001) with its inherent costs for the public health care system but also for the employer (Ricci et al., 2007), in addition to the burden for the individual.Cognitive fatigue can be observed in various domains: Blain et al. (2016) showed that daylong intense cognitive work tends to enhance impulsivity in economic decisions. Likewise, cognitive fatigue has been shown to impair economic decisions, preferences, strategies (Mullette-Gillman et al., 2015), emotion regulation (Grillon et al., 2015), as well as cognitive flexibility in university students (Plukaard et al., 2015). In the sport domain, cognitive fatigue has also been found to alter soccer-specific decision-making (Smith et al., 2016), intermittent running performance (Smith et al., 2015) as well as table tennis performance (Le Mansec et al., 2018).In more severe cases, cognitive fatigue can further develop into a permanent condition, such as Chronic Fatigue Syndrome (CFS; Tanaka & Watanabe, 2010). Cognitive fatigue is also frequently reported in psychological conditions such as depression (Demyttenaere et al., 2005; Lavidor et al., 2002) and neurological illnesses such as Parkinson’s disease (PD), Multiple Sclerosis (MS), traumatic brain injury, stroke, myasthenia gravis, amyotrophic lateral sclerosis, or postpolio syndrome (Chaudhuri & Behan, 2000; Kluger et al., 2013). Obviously, given the potentially tragic consequences of cognitive fatigue, studies are needed to better understand this phenomenon. On the other hand, medical progress has radically increased life expectancy in the last decades, reaching the age of 81.44 in Belgium (in 2017). At the same time, people have been progressively required to work until a more advanced age although diminished cognitive functioning efficiency has been found in older age (Collette & Salmon, 2014; Crawford et al., 2000; Salthouse et al., 2003; West, 1996, 2000). Therefore, it seems crucial to become aware of how cognitive fatigue manifests in advancing age. Surprisingly, very few studies have investigated cognitive fatigue, behaviorally or at the cerebral level, in aging populations.In addition to older age, the middle-aged population also seems particularly at risk for cognitive fatigue. Indeed, midlife has sometimes been considered as the most challenging life period due to the presence of many cognitive requirements (children to care for, work, social life, everyday duties). However, middle-aged people have scarcely been the focus of interest in the literature, probably because of the difficulty reaching this busy population. In an attempt to understand cognitive fatigue at different life stages, studies presented in this Thesis work have systematically focused on three age groups: young, middle-aged, and older people. The first chapter of this Thesis work starts by presenting definitions and models of cognitive fatigue, from those emphasizing energy depletion as the consequence of long-lasting work to those integrating notions that more particularly focus on the voluntarily controlled effort (e.g., executive function, costs/benefits or effort/reward calculation, opportunity cost) invested by the individual into a cognitive activity. For the sake of completeness, this chapter ends by the presentation of some pathological fatigue models. The second chapter describes studies investigating cognitive fatigue in young people. This chapter makes the distinction between experimental protocols based on the Time-on-Task approach (i.e., performing a unique long-lasting task) and those based on the Probe approach (i.e., performing two consecutive tasks in order to test transfer fatigue effects from the first to the second). The presentation of studies also distinguishes between objective (behavioral, electrophysiologic, neuroimaging, connectivity, motivation-related) and subjective (self-reported scales) assessment of cognitive fatigue.The third chapter is dedicated to the presentation of models of cognitive and cerebral aging. It starts by describing cognitive functions that are known to decline with age as well as potential mediators (i.e., processing speed and inhibition) of age-related declines. It presents the well-recognized patterns of cognitive reserve (Stern, 2002, 2009) as well as cerebral compensation postulated in the PASA, ELSA, CRUNCH, and HAROLD hypotheses. It also presents models that more largely integrate factors potentially influencing cognitive aging (Dennis & Cabeza, 2013; STAC; STAC-R) as well as the hypothesis of the declining dopaminergic system. This chapter ends by describing cognitive efficiency in the middle-aged population. The last introductory chapter is dedicated to the presentation of studies about cognitive fatigue in older as well as in middle-aged population. Regarding the experimental part of the Thesis, the first study was based on a Time-on-Task approach in which a 160-minute Stroop task was continuously administrated to young, middle-aged, and older people in order to test performance decrement (increase in extreme reaction times (RTs)) as a function of both the time spent on task and age. The second study was based on the same protocol as the first one, except that rest breaks were given every 40 minutes. This study allowed us to test whether periodically interrupting the task with short breaks (5 minutes) might relieve cognitive fatigue and allow people to maintain performance. The extent to which the three age groups benefit from breaks was also investigated.The third study used a Probe approach in which a fatigue condition (i.e., a long-lasting Stroop task) or a control condition (i.e., watching videos) was directly followed by an N-Back task during functional magnetic resonance imaging (fMRI) acquisition. This procedure allowed us to test whether cerebral activity is differentially modulated by a fatigue state as a function of age. This work ends by a general discussion of the results of the three studies and proposes future lines of investigation in this research field.We hope our results will contribute to advance knowledge about cognitive fatigue in aging and will be the starting point of many other studies afterwards. We already thank all readers for their interest and wish them a compelling reading