175 research outputs found
Editorial: Cognitive inspired aspects of robot learning
[No abstract available
DREAM Architecture: a Developmental Approach to Open-Ended Learning in Robotics
Robots are still limited to controlled conditions, that the robot designer
knows with enough details to endow the robot with the appropriate models or
behaviors. Learning algorithms add some flexibility with the ability to
discover the appropriate behavior given either some demonstrations or a reward
to guide its exploration with a reinforcement learning algorithm. Reinforcement
learning algorithms rely on the definition of state and action spaces that
define reachable behaviors. Their adaptation capability critically depends on
the representations of these spaces: small and discrete spaces result in fast
learning while large and continuous spaces are challenging and either require a
long training period or prevent the robot from converging to an appropriate
behavior. Beside the operational cycle of policy execution and the learning
cycle, which works at a slower time scale to acquire new policies, we introduce
the redescription cycle, a third cycle working at an even slower time scale to
generate or adapt the required representations to the robot, its environment
and the task. We introduce the challenges raised by this cycle and we present
DREAM (Deferred Restructuring of Experience in Autonomous Machines), a
developmental cognitive architecture to bootstrap this redescription process
stage by stage, build new state representations with appropriate motivations,
and transfer the acquired knowledge across domains or tasks or even across
robots. We describe results obtained so far with this approach and end up with
a discussion of the questions it raises in Neuroscience
Deep learning for video game playing
In this article, we review recent Deep Learning advances in the context of
how they have been applied to play different types of video games such as
first-person shooters, arcade games, and real-time strategy games. We analyze
the unique requirements that different game genres pose to a deep learning
system and highlight important open challenges in the context of applying these
machine learning methods to video games, such as general game playing, dealing
with extremely large decision spaces and sparse rewards
Novel perspectives on the causal mind:Experiments, modeling, and theory
This thesis presents research into human causal cognition using a variety of perspectives and methodologies. I surveyed the existing literature on causal cognition and identified shortcomings, paying particular attention to different methodologies (from psychology, cognitive science, logic, and philosophy). This text is subdivided into three parts, each of which presents work using novel methods in a different field. These fields are 1) experimental psychology, 2) computational cognitive modelling, and 3) philosophy. In part 1 I present two experiments on causal reasoning where I teach participants causal network information and then ask them to solve inference problems in the form of causal probabilistic queries (e.g.: if X causes A and B, what is the probability of A being present knowing that X is but B is not present?). The first experiment focusses on the effect of time pressure on such causal judgements, while the second experiment uses multiple techniques to elicit repeated judgments for participants in order to assess both inter- and intra-participant variability in causal judgments. In the second part of the dissertation, we develop and test a new cognitive model of causal reasoning named the Bayesian Mutation Sampler. The first chapter in this section discusses the rationale behind the Bayesian Mutation Sampler and shows how it is an improvement over the model it is based on (the Mutation Sampler). In the next chapter I employ cognitive modelling to account for the inter- and intra-participant variability in causal judgments. This study confirms that the Bayesian Mutation Sampler outperforms other plausible models. In part 3 I take a radical turn towards philosophy. I identify, and subsequently build upon, a lack of an embodied and situated perspective on causal cognition. In this part I first give an introduction to the Skilled Intentionality Framework, which I then use to put forward an affordance-based theory of causal cognition which I develop using the literature on embodied cognition and ecological psychology
Towards Continual Reinforcement Learning: A Review and Perspectives
In this article, we aim to provide a literature review of different
formulations and approaches to continual reinforcement learning (RL), also
known as lifelong or non-stationary RL. We begin by discussing our perspective
on why RL is a natural fit for studying continual learning. We then provide a
taxonomy of different continual RL formulations and mathematically characterize
the non-stationary dynamics of each setting. We go on to discuss evaluation of
continual RL agents, providing an overview of benchmarks used in the literature
and important metrics for understanding agent performance. Finally, we
highlight open problems and challenges in bridging the gap between the current
state of continual RL and findings in neuroscience. While still in its early
days, the study of continual RL has the promise to develop better incremental
reinforcement learners that can function in increasingly realistic applications
where non-stationarity plays a vital role. These include applications such as
those in the fields of healthcare, education, logistics, and robotics.Comment: Preprint, 52 pages, 8 figure
Similarity Reasoning over Semantic Context-Graphs
Similarity is a central cognitive mechanism for humans which enables a broad range of perceptual and abstraction processes, including recognizing and categorizing objects, drawing parallelism, and predicting outcomes. It has been studied computationally through models designed to replicate human judgment. The work presented in this dissertation leverages general purpose semantic networks to derive similarity measures in a problem-independent manner. We model both general and relational similarity using connectivity between concepts within semantic networks. Our first contribution is to model general similarity using concept connectivity, which we use to partition vocabularies into topics without the need of document corpora. We apply this model to derive topics from unstructured dialog, specifically enabling an early literacy primer application to support parents in having better conversations with their young children, as they are using the primer together. Second, we model relational similarity in proportional analogies. To do so, we derive relational parallelism by searching in semantic networks for similar path pairs that connect either side of this analogy statement. We then derive human readable explanations from the resulting similar path pair. We show that our model can answer broad-vocabulary analogy questions designed for human test takers with high confidence. The third contribution is to enable symbolic plan repair in robot planning through object substitution. When a failure occurs due to unforeseen changes in the environment, such as missing objects, we enable the planning domain to be extended with a number of alternative objects such that the plan can be repaired and execution to continue. To evaluate this type of similarity, we use both general and relational similarity. We demonstrate that the task context is essential in establishing which objects are interchangeable
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