Considerations in designing a cybernetic simple 'learning' model; and an overview of the problem of modelling learning

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Learning is viewed as a central feature of living systems and must be manifested in any artifact that claims to exhibit general intelligence. The central aims of the thesis are twofold: (1) - To review and critically assess the empirical and theoretical aspects of learning as have been addressed in a multitude of disciplines, with the aim of extracting fundamental features and elements. (2) - To develop a more systematic approach to the cybernetic modelling of learning than has been achieved hitherto. In pursuit of aim (1) above the following discussions are included: Historical and Philosophical backgrounds; Natural learning, both physiological and psychological aspects; Hierarchies of learning identified in the evolutionary, functional and developmental senses; An extensive section on the general problem of modelling of learning and the formal tools, is included as a link between aims (1) and (2). Following this a systematic and historically oriented study of cybernetic and other related approaches to the problem of modelling of learning is presented. This then leads to the development of a state-of-the-art general purpose experimental cybernetic learning model. The programming and use of this model is also fully described, including an elaborate scheme for the manifestation of simple learning

Simulating activities: Relating motives, deliberation, and attentive coordination

Activities are located behaviors, taking time, conceived as socially meaningful, and usually involving interaction with tools and the environment. In modeling human cognition as a form of problem solving (goal-directed search and operator sequencing), cognitive science researchers have not adequately studied “off-task” activities (e.g., waiting), non-intellectual motives (e.g., hunger), sustaining a goal state (e.g., playful interaction), and coupled perceptual-motor dynamics (e.g., following someone). These aspects of human behavior have been considered in bits and pieces in past research, identified as scripts, human factors, behavior settings, ensemble, flow experience, and situated action. More broadly, activity theory provides a comprehensive framework relating motives, goals, and operations. This paper ties these ideas together, using examples from work life in a Canadian High Arctic research station. The emphasis is on simulating human behavior as it naturally occurs, such that “working” is understood as an aspect of living. The result is a synthesis of previously unrelated analytic perspectives and a broader appreciation of the nature of human cognition. Simulating activities in this comprehensive way is useful for understanding work practice, promoting learning, and designing better tools, including human-robot systems

SciTech News Volume 71, No. 1 (2017)

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Autonomy of Military Robots: Assessing the Technical and Legal (“Jus In Bello”) Thresholds, 32 J. Marshall J. Info. Tech. & Privacy L. 57 (2016)

While robots are still absent from our homes, they have started to spread over battlefields. However, the military robots of today are mostly remotely controlled platforms, with no real autonomy. This paper will disclose the obstacles in implementing autonomy for such systems by answering a technical question: What level of autonomy is needed in military robots and how and when might it be achieved, followed by a techno-legal one: How to implement the rules of humanitarian law within autonomous fighting robots, in order to allow their legal deployment? The first chapter scrutinizes the significance of autonomy in robots and the metrics used to quantify it, which were developed by the US Department of Defense. The second chapter focuses on the autonomy of state-of-the-art” robots (e.g.; Google’s self-driving car, DARPA’s projects, etc.) for navigation, ISR or lethal missions. Based on public information, we will get a hint of the architectures, the functioning, the thresholds and technical limitations of such systems. The bottleneck to a higher autonomy of robots seems to be their poor “perceptive intelligence.” The last chapter looks to the requirements of humanitarian law (rules of “jus in bello”/rules of engagement) to the legal deployment of autonomous lethal robots on the battlefields. The legal and moral reasoning of human soldiers, complying with humanitarian law, is a complex cognitive process which must be emulated by autonomous robots that could make lethal decisions. However, autonomous completion of such “moral” tasks by artificial agents is much more challenging than the autonomous implementation of other tasks, such as navigation, ISR or kinetic attacks. Given the limits of current Artificial Intelligence, it is highly unlikely that robots will acquire such moral capabilities anytime soon. Therefore, for the time being, the autonomous weapon systems might be legally deployed, but only in very particular circumstances, where the requirements of humanitarian law happen to be irrelevant

A Survey of Knowledge Representation in Service Robotics

Within the realm of service robotics, researchers have placed a great amount of effort into learning, understanding, and representing motions as manipulations for task execution by robots. The task of robot learning and problem-solving is very broad, as it integrates a variety of tasks such as object detection, activity recognition, task/motion planning, localization, knowledge representation and retrieval, and the intertwining of perception/vision and machine learning techniques. In this paper, we solely focus on knowledge representations and notably how knowledge is typically gathered, represented, and reproduced to solve problems as done by researchers in the past decades. In accordance with the definition of knowledge representations, we discuss the key distinction between such representations and useful learning models that have extensively been introduced and studied in recent years, such as machine learning, deep learning, probabilistic modelling, and semantic graphical structures. Along with an overview of such tools, we discuss the problems which have existed in robot learning and how they have been built and used as solutions, technologies or developments (if any) which have contributed to solving them. Finally, we discuss key principles that should be considered when designing an effective knowledge representation.Comment: Accepted for RAS Special Issue on Semantic Policy and Action Representations for Autonomous Robots - 22 Page