Learning object relationships which determine the outcome of actions

Peer reviewedPublisher PD

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

Technological Spaces: An Initial Appraisal

In this paper, we propose a high level view of technological spaces (TS) and relations among these spaces. A technological space is a working context with a set of associated concepts, body of knowledge, tools, required skills, and possibilities. It is often associated to a given user community with shared know-how, educational support, common literature and even workshop and conference regular meetings. Although it is difficult to give a precise definition, some TSs can be easily identified, e.g. the XML TS, the DBMS TS, the abstract syntax TS, the meta-model (OMG/MDA) TS, etc. The purpose of our work is not to define an abstract theory of technological spaces, but to figure out how to work more efficiently by using the best possibilities of each technology. To do so, we need a basic understanding of the similarities and differences between various TSs, and also of the possible operational bridges that will allow transferring the results obtained in one TS to other TS. We hope that the presented industrial vision may help us putting forward the idea that there could be more cooperation than competition among alternative technologies. Furthermore, as the spectrum of such available technologies is rapidly broadening, the necessity to offer clear guidelines when choosing practical solutions to engineering problems is becoming a must, not only for teachers but for project leaders as well

Predicting Anchor Links between Heterogeneous Social Networks

People usually get involved in multiple social networks to enjoy new services or to fulfill their needs. Many new social networks try to attract users of other existing networks to increase the number of their users. Once a user (called source user) of a social network (called source network) joins a new social network (called target network), a new inter-network link (called anchor link) is formed between the source and target networks. In this paper, we concentrated on predicting the formation of such anchor links between heterogeneous social networks. Unlike conventional link prediction problems in which the formation of a link between two existing users within a single network is predicted, in anchor link prediction, the target user is missing and will be added to the target network once the anchor link is created. To solve this problem, we use meta-paths as a powerful tool for utilizing heterogeneous information in both the source and target networks. To this end, we propose an effective general meta-path-based approach called Connector and Recursive Meta-Paths (CRMP). By using those two different categories of meta-paths, we model different aspects of social factors that may affect a source user to join the target network, resulting in the formation of a new anchor link. Extensive experiments on real-world heterogeneous social networks demonstrate the effectiveness of the proposed method against the recent methods.Comment: To be published in "Proceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)

Do they practice what we teach? Follow-up evaluation of a Schema Therapy training programme

This study evaluated a three-day Schema Therapy training programme for trainee clinical psychologists. The training used an experiential model of learning, which was intended to encourage the transfer of knowledge and techniques from the learning environment into clinical practice. Using a mixed-methods approach, the training programme was evaluated in terms of: (1) self-reported changes in knowledge, confidence and willingness to use Schema Therapy-informed techniques; (2) whether the training was integrated into clinical practice; and (3) the perceived barriers/facilitators to achieving practice integration. Participants – 17 of the 19 trainee clinical psychologists enrolled on the Schema Therapy training programme – completed assessments immediately pre- and post-training. Participants were subsequently followed-up for reassessment three months after the training. Group- and individual-level analyses showed that most participants reported training-related gains in knowledge and confidence; these were largely sustained at follow-up, and were associated with post-training practice integration of Schema Therapy concepts and techniques. Analysis of qualitative data identified factors moderating use of training in practice. Findings of the study have implications for future delivery and evaluation of training in cognitive-behavioural therapies

Do they practice what we teach? Follow-up evaluation of a Schema Therapy training programme

This study evaluated a three-day Schema Therapy training programme for trainee clinical psychologists. The training used an experiential model of learning, which was intended to encourage the transfer of knowledge and techniques from the learning environment into clinical practice. Using a mixed-methods approach, the training programme was evaluated in terms of: (1) self-reported changes in knowledge, confidence and willingness to use Schema Therapy-informed techniques; (2) whether the training was integrated into clinical practice; and (3) the perceived barriers/facilitators to achieving practice integration. Participants – 17 of the 19 trainee clinical psychologists enrolled on the Schema Therapy training programme – completed assessments immediately pre- and post-training. Participants were subsequently followed-up for reassessment three months after the training. Group- and individual-level analyses showed that most participants reported training-related gains in knowledge and confidence; these were largely sustained at follow-up, and were associated with post-training practice integration of Schema Therapy concepts and techniques. Analysis of qualitative data identified factors moderating use of training in practice. Findings of the study have implications for future delivery and evaluation of training in cognitive-behavioural therapies