Drama, a connectionist model for robot learning: experiments on grounding communication through imitation in autonomous robots

The present dissertation addresses problems related to robot learning from demonstra¬ tion. It presents the building of a connectionist architecture, which provides the robot with the necessary cognitive and behavioural mechanisms for learning a synthetic lan¬ guage taught by an external teacher agent. This thesis considers three main issues: 1) learning of spatio-temporal invariance in a dynamic noisy environment, 2) symbol grounding of a robot's actions and perceptions, 3) development of a common symbolic representation of the world by heterogeneous agents.We build our approach on the assumption that grounding of symbolic communication creates constraints not only on the cognitive capabilities of the agent but also and especially on its behavioural capacities. Behavioural skills, such as imitation, which allow the agent to co-ordinate its actionn to that of the teacher agent, are required aside to general cognitive abilities of associativity, in order to constrain the agent's attention to making relevant perceptions, onto which it grounds the teacher agent's symbolic expression. In addition, the agent should be provided with the cognitive capacity for extracting spatial and temporal invariance in the continuous flow of its perceptions. Based on this requirement, we develop a connectionist architecture for learning time series. The model is a Dynamical Recurrent Associative Memory Architecture, called DRAMA. It is a fully connected recurrent neural network using Hebbian update rules. Learning is dynamic and unsupervised. The performance of the architecture is analysed theoretically, through numerical simulations and through physical and simulated robotic experiments. Training of the network is computationally fast and inexpensive, which allows its implementation for real time computation and on-line learning in a inexpensive hardware system. Robotic experiments are carried out with different learning tasks involving recognition of spatial and temporal invariance, namely landmark recognition and prediction of perception-action sequence in maze travelling.The architecture is applied to experiments on robot learning by imitation. A learner robot is taught by a teacher agent, a human instructor and another robot, a vocabulary to describe its perceptions and actions. The experiments are based on an imitative strategy, whereby the learner robot reproduces the teacher's actions. While imitating the teacher's movements, the learner robot makes similar proprio and exteroceptions to those of the teacher. The learner robot grounds the teacher's words onto the set of common perceptions they share. We carry out experiments in simulated and physical environments, using different robotic set-ups, increasing gradually the complexity of the task. In a first set of experiments, we study transmission of a vocabulary to designate actions and perception of a robot. Further, we carry out simulation studies, in which we investigate transmission and use of the vocabulary among a group of robotic agents. In a third set of experiments, we investigate learning sequences of the robot's perceptions, while wandering in a physically constrained environment. Finally, we present the implementation of DRAMA in Robota, a doll-like robot, which can imitate the arms and head movements of a human instructor. Through this imitative game, Robota is taught to perform and label dance patterns. Further, Robota is taught a basic language, including a lexicon and syntactical rules for the combination of words of the lexicon, to describe its actions and perception of touch onto its body

Expectations and expertise in artificial intelligence: specialist views and historical perspectives on conceptualisation, promise, and funding

Artificial intelligence’s (AI) distinctiveness as a technoscientific field that imitates the ability to think went through a resurgence of interest post-2010, attracting a flood of scientific and popular expectations as to its utopian or dystopian transformative consequences. This thesis offers observations about the formation and dynamics of expectations based on documentary material from the previous periods of perceived AI hype (1960-1975 and 1980-1990, including in-between periods of perceived dormancy), and 25 interviews with UK-based AI specialists, directly involved with its development, who commented on the issues during the crucial period of uncertainty (2017-2019) and intense negotiation through which AI gained momentum prior to its regulation and relatively stabilised new rounds of long-term investment (2020-2021). This examination applies and contributes to longitudinal studies in the sociology of expectations (SoE) and studies of experience and expertise (SEE) frameworks, proposing a historical sociology of expertise and expectations framework. The research questions, focusing on the interplay between hype mobilisation and governance, are: (1) What is the relationship between AI practical development and the broader expectational environment, in terms of funding and conceptualisation of AI? (2) To what extent does informal and non-developer assessment of expectations influence formal articulations of foresight? (3) What can historical examinations of AI’s conceptual and promissory settings tell about the current rebranding of AI? The following contributions are made: (1) I extend SEE by paying greater attention to the interplay between technoscientific experts and wider collective arenas of discourse amongst non-specialists and showing how AI’s contemporary research cultures are overwhelmingly influenced by the hype environment but also contribute to it. This further highlights the interaction between competing rationales focusing on exploratory, curiosity-driven scientific research against exploitation-oriented strategies at formal and informal levels. (2) I suggest benefits of examining promissory environments in AI and related technoscientific fields longitudinally, treating contemporary expectations as historical products of sociotechnical trajectories through an authoritative historical reading of AI’s shifting conceptualisation and attached expectations as a response to availability of funding and broader national imaginaries. This comes with the benefit of better perceiving technological hype as migrating from social group to social group instead of fading through reductionist cycles of disillusionment; either by rebranding of technical operations, or by the investigation of a given field by non-technical practitioners. It also sensitises to critically examine broader social expectations as factors for shifts in perception about theoretical/basic science research transforming into applied technological fields. Finally, (3) I offer a model for understanding the significance of interplay between conceptualisations, promising, and motivations across groups within competing dynamics of collective and individual expectations and diverse sources of expertise

How does rumination impact cognition? A first mechanistic model.

Rumination is a process of uncontrolled, narrowly-foused neg- ative thinking that is often self-referential, and that is a hall- mark of depression. Despite its importance, little is known about its cognitive mechanisms. Rumination can be thought of as a specific, constrained form of mind-wandering. Here, we introduce a cognitive model of rumination that we devel- oped on the basis of our existing model of mind-wandering. The rumination model implements the hypothesis that rumina- tion is caused by maladaptive habits of thought. These habits of thought are modelled by adjusting the number of memory chunks and their associative structure, which changes the se- quence of memories that are retrieved during mind-wandering, such that during rumination the same set of negative memo- ries is retrieved repeatedly. The implementation of habits of thought was guided by empirical data from an experience sam- pling study in healthy and depressed participants. On the ba- sis of this empirically-derived memory structure, our model naturally predicts the declines in cognitive task performance that are typically observed in depressed patients. This study demonstrates how we can use cognitive models to better un- derstand the cognitive mechanisms underlying rumination and depression

Artificial general intelligence: Proceedings of the Second Conference on Artificial General Intelligence, AGI 2009, Arlington, Virginia, USA, March 6-9, 2009

Artificial General Intelligence (AGI) research focuses on the original and ultimate goal of AI – to create broad human-like and transhuman intelligence, by exploring all available paths, including theoretical and experimental computer science, cognitive science, neuroscience, and innovative interdisciplinary methodologies. Due to the difficulty of this task, for the last few decades the majority of AI researchers have focused on what has been called narrow AI – the production of AI systems displaying intelligence regarding specific, highly constrained tasks. In recent years, however, more and more researchers have recognized the necessity – and feasibility – of returning to the original goals of the field. Increasingly, there is a call for a transition back to confronting the more difficult issues of human level intelligence and more broadly artificial general intelligence

The design and evaluation of an interface and control system for a scariculated rehabilitation robot arm

This thesis is concerned with the design and development of a prototype implementation of a Rehabilitation Robotic manipulator based on a novel kinematic configuration. The initial aim of the research was to identify appropriate design criteria for the design of a user interface and control system, and for the subsequent evaluation of the manipulator prototype. This led to a review of the field of rehabilitation robotics, focusing on user evaluations of existing systems. The review showed that the design objectives of individual projects were often contradictory, and that a requirement existed for a more general and complete set of design criteria. These were identified through an analysis of the strengths and weaknesses of existing systems, including an assessment of manipulator performances, commercial success and user feedback. The resulting criteria were used for the design and development of a novel interface and control system for the Middlesex Manipulator - the novel scariculated robotic system. A highly modular architecture was adopted, allowing the manipulator to provide a level of adaptability not approached by existing rehabilitation robotic systems. This allowed the interface to be configured to match the controlling ability and input device selections of individual users. A range of input devices was employed, offering variation in communication mode and bandwidth. These included a commercial voice recognition system, and a novel gesture recognition device. The later was designed using electrolytic tilt sensors, the outputs of which were encoded by artificial neural networks. These allowed for control of the manipulator through head or hand gestures. An individual with spinal-cord injury undertook a single-subject user evaluation of the Middlesex Manipulator over a period of four months. The evaluation provided evidence for the value of adaptability presented by the user interface. It was also shown that the prototype did not currently confonn to all the design criteria, but allowed for the identification of areas for design improvements. This work led to a second research objective, concerned with the problem of configuring an adaptable user interface for a specific individual. A novel form of task analysis is presented within the thesis, that allows the relative usability of interface configurations to be predicted based upon individual user and input device characteristics. An experiment was undertaken with 6 subjects performing 72 tasks runs with 2 interface configurations controlled by user gestures. Task completion times fell within the range predicted, where the range was generated using confidence intervals (α = 0.05) on point estimates of user and device characteristics. This allowed successful prediction over all task runs of the relative task completion times of interface configurations for a given user