Intrinsic Motivation Systems for Autonomous Mental Development

Exploratory activities seem to be intrinsically rewarding for children and crucial for their cognitive development. Can a machine be endowed with such an intrinsic motivation system? This is the question we study in this paper, presenting a number of computational systems that try to capture this drive towards novel or curious situations. After discussing related research coming from developmental psychology, neuroscience, developmental robotics, and active learning, this paper presents the mechanism of Intelligent Adaptive Curiosity, an intrinsic motivation system which pushes a robot towards situations in which it maximizes its learning progress. This drive makes the robot focus on situations which are neither too predictable nor too unpredictable, thus permitting autonomous mental development.The complexity of the robot’s activities autonomously increases and complex developmental sequences self-organize without being constructed in a supervised manner. Two experiments are presented illustrating the stage-like organization emerging with this mechanism. In one of them, a physical robot is placed on a baby play mat with objects that it can learn to manipulate. Experimental results show that the robot first spends time in situations which are easy to learn, then shifts its attention progressively to situations of increasing difficulty, avoiding situations in which nothing can be learned. Finally, these various results are discussed in relation to more complex forms of behavioral organization and data coming from developmental psychology. Key words: Active learning, autonomy, behavior, complexity, curiosity, development, developmental trajectory, epigenetic robotics, intrinsic motivation, learning, reinforcement learning, values

The Future Evolution of Consciousness

ABSTRACT. What potential exists for improvements in the functioning of consciousness? The paper addresses this issue using global workspace theory. According to this model, the prime function of consciousness is to develop novel adaptive responses. Consciousness does this by putting together new combinations of knowledge, skills and other disparate resources that are recruited from throughout the brain. The paper’s search for potential improvements in the functioning of consciousness draws on studies of the shift during human development from the use of implicit knowledge to the use of explicit (declarative) knowledge. These studies show that the ability of consciousness to adapt a particular domain improves significantly as the transition to the use of declarative knowledge occurs in that domain. However, this potential for consciousness to enhance adaptability has not yet been realised to any extent in relation to consciousness itself. The paper assesses the potential for adaptability to be improved by the conscious adaptation of key processes that constitute consciousness. A number of sources (including the practices of religious and contemplative traditions) are drawn on to investigate how this potential might be realised

Strategic marketing planning : a state of the art review

School of Managemen

Local search: A guide for the information retrieval practitioner

There are a number of combinatorial optimisation problems in information retrieval in which the use of local search methods are worthwhile. The purpose of this paper is to show how local search can be used to solve some well known tasks in information retrieval (IR), how previous research in the field is piecemeal, bereft of a structure and methodologically flawed, and to suggest more rigorous ways of applying local search methods to solve IR problems. We provide a query based taxonomy for analysing the use of local search in IR tasks and an overview of issues such as fitness functions, statistical significance and test collections when conducting experiments on combinatorial optimisation problems. The paper gives a guide on the pitfalls and problems for IR practitioners who wish to use local search to solve their research issues, and gives practical advice on the use of such methods. The query based taxonomy is a novel structure which can be used by the IR practitioner in order to examine the use of local search in IR

Howard Gardner: the myth of multiple intelligences

This evening I want to examine Howard Gardner’s idea that there are multiple intelligences (MI theory). MI theory identifies some eight or nine types of intelligence: not only the logico-mathematical and linguistic kinds measured by IQ

PID control system analysis, design, and technology

Designing and tuning a proportional-integral-derivative (PID) controller appears to be conceptually intuitive, but can be hard in practice, if multiple (and often conflicting) objectives such as short transient and high stability are to be achieved. Usually, initial designs obtained by all means need to be adjusted repeatedly through computer simulations until the closed-loop system performs or compromises as desired. This stimulates the development of "intelligent" tools that can assist engineers to achieve the best overall PID control for the entire operating envelope. This development has further led to the incorporation of some advanced tuning algorithms into PID hardware modules. Corresponding to these developments, this paper presents a modern overview of functionalities and tuning methods in patents, software packages and commercial hardware modules. It is seen that many PID variants have been developed in order to improve transient performance, but standardising and modularising PID control are desired, although challenging. The inclusion of system identification and "intelligent" techniques in software based PID systems helps automate the entire design and tuning process to a useful degree. This should also assist future development of "plug-and-play" PID controllers that are widely applicable and can be set up easily and operate optimally for enhanced productivity, improved quality and reduced maintenance requirements

The elements of design knowledge capture

This paper will present the basic constituents of a design knowledge capture effort. This will include a discussion of the types of knowledge to be captured in such an effort and the difference between design knowledge capture and more traditional knowledge base construction. These differences include both knowledge base structure and knowledge acquisition approach. The motivation for establishing a design knowledge capture effort as an integral part of major NASA programs will be outlined, along with the current NASA position on that subject. Finally the approach taken in design knowledge capture for Space Station will be contrasted with that used in the HSTDEK project

One Knowledge Base or Many Knowledge Pools?

It is increasingly realized that knowledge is the most important resource and that learning is the most important process in the economy. Sometimes this is expressed by coining the current era as characterised by a ‘knowledge based economy’. But this concept might be misleading by indicating that there is one common knowledge base on which economic activities can be built. In this paper we argue that it is more appropriate to see the economy as connecting to different ‘pools of knowledge’. The argument is built upon a conceptual framework where we make distinctions between private/public, local/global, individual/collective and tacit/codified knowledge. The purpose is both ‘academic’ and practical. Our analysis demonstrates the limits of a narrowly economic perspective on knowledge and we show that these distinctions have important implications both for innovation policy and for management of innovation.Knowledge, economic development

Prospects for computational steering of evolutionary computation

Currently, evolutionary computation (EC) typically takes place in batch mode: algorithms are run autonomously, with the user providing little or no intervention or guidance. Although it is rarely possible to specify in advance, on the basis of EC theory, the optimal evolutionary algorithm for a particular problem, it seems likely that experienced EC practitioners possess considerable tacit knowledge of how evolutionary algorithms work. In situations such as this, computational steering (ongoing, informed user intervention in the execution of an otherwise autonomous computational process) has been profitably exploited to improve performance and generate insights into computational processes. In this short paper, prospects for the computational steering of evolutionary computation are assessed, and a prototype example of computational steering applied to a coevolutionary algorithm is presented