A Cognitive Robotic Imitation Learning System Based On Cause-Effect Reasoning

As autonomous systems become more intelligent and ubiquitous, it is increasingly important that their behavior can be easily controlled and understood by human end users. Robotic imitation learning has emerged as a useful paradigm for meeting this challenge. However, much of the research in this area focuses on mimicking the precise low-level motor control of a demonstrator, rather than interpreting the intentions of a demonstrator at a cognitive level, which limits the ability of these systems to generalize. In particular, cause-effect reasoning is an important component of human cognition that is under-represented in these systems. This dissertation contributes a novel framework for cognitive-level imitation learning that uses parsimonious cause-effect reasoning to generalize demonstrated skills, and to justify its own actions to end users. The contributions include new causal inference algorithms, which are shown formally to be correct and have reasonable computational complexity characteristics. Additionally, empirical validations both in simulation and on board a physical robot show that this approach can efficiently and often successfully infer a demonstrator’s intentions on the basis of a single demonstration, and can generalize learned skills to a variety of new situations. Lastly, computer experiments are used to compare several formal criteria of parsimony in the context of causal intention inference, and a new criterion proposed in this work is shown to compare favorably with more traditional ones. In addition, this dissertation takes strides towards a purely neurocomputational implementation of this causally-driven imitation learning framework. In particular, it contributes a novel method for systematically locating fixed points in recurrent neural networks. Fixed points are relevant to recent work on neural networks that can be “programmed” to exhibit cognitive-level behaviors, like those involved in the imitation learning system developed here. As such, the fixed point solver developed in this work is a tool that can be used to improve our engineering and understanding of neurocomputational cognitive control in the next generation of autonomous systems, ultimately resulting in systems that are more pliable and transparent

Cognition in Aristotle's Poetics

This paper examines Aristotle’s understanding of the contributions of perceptual and rational cognition to the composition and reception of poetry. An initial outline of Aristotle’s cognitive psychology shows that Aristotelian perception is sufficiently powerful to sustain very rich, complex patterns of behaviour in human as well as non-human animals, and examines the interaction between perception (cognition of the particular and the ‘that’) and the distinctive capacity for reason (which makes possible cognition of the universal and the ‘why’) in human behaviour. The rest of the paper applies this framework to a number of problems in the Poetics: (i) If Aristotelian tekhnê is defined as a productive disposition involving reason, how can poetic tekhnê be manifested in the work of poets who work by non-rational habit or talent? (ii) Why does Aristotle believe that the pleasure taken in imitation qua imitation involves rational inference? (iii) What does Aristotle mean when he contrasts history (concerned with the particular) and poetry (concerned with the universal)? (iv) How is Aristotle’s insistence on universality and rationality in the construction of poetic plots to be reconciled with his willingness to tolerate irrationalities and implausibilities

Is It Rational to Assume that Infants Imitate Rationally? A Theoretical Analysis and Critique

It has been suggested that preverbal infants evaluate the efficiency of others' actions (by applying a principle of rational action) and that they imitate others' actions rationally. The present contribution presents a conceptual analysis of the claim that preverbal infants imitate rationally. It shows that this ability rests on at least three assumptions: that infants are able to perceive others' action capabilities, that infants reason about and conceptually represent their own bodies, and that infants are able to think counterfactually. It is argued that none of these three abilities is in place during infancy. Furthermore, it is shown that the idea of a principle of rational action suffers from two fallacies. As a consequence, is it suggested that it is not rational to assume that infants imitate rationally. Copyright (C) 2012 S. Karger AG, Base

Human Minds

Articl

Introduction: The Third International Conference on Epigenetic Robotics

This paper summarizes the paper and poster contributions to the Third International Workshop on Epigenetic Robotics. The focus of this workshop is on the cross-disciplinary interaction of developmental psychology and robotics. Namely, the general goal in this area is to create robotic models of the psychological development of various behaviors. The term "epigenetic" is used in much the same sense as the term "developmental" and while we could call our topic "developmental robotics", developmental robotics can be seen as having a broader interdisciplinary emphasis. Our focus in this workshop is on the interaction of developmental psychology and robotics and we use the phrase "epigenetic robotics" to capture this focus

A comparison of techniques for learning and using mathematics and a study of their relationship to logical principles

Various techniques exist for learning mathematical concepts, like experimentation and exploration, respectively using mathematics, like modelling and simulation. For a clear application of such techniques in mathematics education, there should be a clear distinction between these techniques. A recently developed theory of fuzzy concepts can be applied to analyse the four mentioned concepts. For all four techniques one can pose the question of their relationship to deduction, induction and abduction as logical principles. An empirical study was conducted with 12-13 aged students, aiming at checking the three reasoning processes

Adversarial Connective-exploiting Networks for Implicit Discourse Relation Classification

Implicit discourse relation classification is of great challenge due to the lack of connectives as strong linguistic cues, which motivates the use of annotated implicit connectives to improve the recognition. We propose a feature imitation framework in which an implicit relation network is driven to learn from another neural network with access to connectives, and thus encouraged to extract similarly salient features for accurate classification. We develop an adversarial model to enable an adaptive imitation scheme through competition between the implicit network and a rival feature discriminator. Our method effectively transfers discriminability of connectives to the implicit features, and achieves state-of-the-art performance on the PDTB benchmark.Comment: To appear in ACL201