Prediction of intent in robotics and multi-agent systems.

Moving beyond the stimulus contained in observable agent behaviour, i.e. understanding the underlying intent of the observed agent is of immense interest in a variety of domains that involve collaborative and competitive scenarios, for example assistive robotics, computer games, robot-human interaction, decision support and intelligent tutoring. This review paper examines approaches for performing action recognition and prediction of intent from a multi-disciplinary perspective, in both single robot and multi-agent scenarios, and analyses the underlying challenges, focusing mainly on generative approaches

Five dimensions in the communication of design intent

Industries which utilize Computer Aided Design, (CAD), are in a similar situation to the film industry, where the use of Computer Graphics, (CG), has reached such a level of reality that audiences often do not spot where CG has been used. This has resulted in a general attitude among critics of: “CG is what you expect in a film, but what we often lack is a decent plot”. Over a similar period, CAD software has become a powerful tool with proficient users, whilst the marketplace for such services now takes such facilities for granted. The ‘wow factor’ has faded. The special effects used in films has contributed to this dulling of presentation impact, which leads us to question where we stand in relation to a competitive edge, with the realization that: “CAD is what you expect from a firm, but what we often lack is clear intent.” The questioning of competitive edge draws us into some complex issues, concerning the reduction of compromise for design intent, where priorities fight for first place. There is no disputing the importance of time to market, yet the time compression technologies may no longer be providing a sufficient cutting edge. Even if new technologies facilitate even shorter lead-times we will always face the threat of a time management trap and potential loss of design quality. As a high-risk strategy for competitive advantage, contractual agreements for specified short lead-time deliveries, in some cases with penalty clauses written in, have established an expectation among the client base. Such a strategy leads us to effectively burn our bridges, in sacrificing margins for schedule 3 slippage and error compensation, leaving us nowhere to go but back. With such a lean approach to product development we have to improve our focus on the plot and its intent for design quality. The more investment we make at the front end, to enable the decision making process, the more likely we are to avoid pain at the back-end. Presently, decisions are made on a resource of available quality and quantity of data, using a perspective which is based on the experience, tacit knowledge and intuition of those involved. Whilst intuition is a good starting point or fall-back, as with tacit knowledge, it often proves difficult to substantiate. Background experience is the most valuable asset here but proves ineffectual when faced with low quality data, either through ambiguity, error or lack of substance. The improvement of quality standards require that we look closely at the production and presentation of data in the context of decision making and establish a process by which quality decisions can be made quickly and efficiently. This paper focuses on the process of communication between designers and their colleagues and clients, concerning the presentation of CAD models, from a cognitive perspective. It first establishes a context for individual differences in the management of auditory and visual information for decision making. This is followed by a discussion of five approaches to the communication of design intent and concludes with a checklist, to aid selection of an effective approach to communication

A competitive environment for exploratory query expansion

Most information workers query digital libraries many times a day. Yet people have little opportunity to hone their skills in a controlled environment, or compare their performance with others in an objective way. Conversely, although search engine logs record how users evolve queries, they lack crucial information about the user's intent. This paper describes an environment for exploratory query expansion that pits users against each other and lets them compete, and practice, in their own time and on their own workstation. The system captures query evolution behavior on predetermined information-seeking tasks. It is publicly available, and the code is open source so that others can set up their own competitive environments

Cooperation through Imitation

This paper characterizes long-run outcomes for broad classes of symmetric games, when players select actions on the basis of average historical performance. Received wisdom is that when agent's interests are partially opposed, behavior is excessively competitive: ``keeping up with the Jones' '' lowers everyones' welfare. Here, we study the long-run consequences of imitative behavior when agents have sufficiently long memories --- and the outcome is dramatically different. Imitation robustly leads to cooperative outcomes (with highest symmetric payoffs) in the long run. This provides a rationale, for example, for collusive cartel-like behavior without collusive intent on the part of the agents.Evolution, Imitation

LOMo: Latent Ordinal Model for Facial Analysis in Videos

We study the problem of facial analysis in videos. We propose a novel weakly supervised learning method that models the video event (expression, pain etc.) as a sequence of automatically mined, discriminative sub-events (eg. onset and offset phase for smile, brow lower and cheek raise for pain). The proposed model is inspired by the recent works on Multiple Instance Learning and latent SVM/HCRF- it extends such frameworks to model the ordinal or temporal aspect in the videos, approximately. We obtain consistent improvements over relevant competitive baselines on four challenging and publicly available video based facial analysis datasets for prediction of expression, clinical pain and intent in dyadic conversations. In combination with complimentary features, we report state-of-the-art results on these datasets.Comment: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR

COMPETITIVE EFFECTS OF IT INNOVATION ON BANK STRATEGY, 1985-1995

Through case study research this paper illustrates opportunities presented by IT-based technological change in British retail bank markets (1985-1995). For the managers of the Royal Bank of Scotland IT appeared to lower entry barriers, exit barriers and deliver high sustainability of competitive advantage. The strategic intent behind diversification patterns of the Royal Bank of Scotland suggested competitive considerations were at a premium because unsolicited take-over bids in the early 1980s put pressure on managers to create growth opportunities. Direct Line Insurance was a subsidiary from the Royal Bank of Scotland. Direct Line was also the first retail finance institution to establish a clear competitive advantage based on information technology. The success of Direct Line enabled an increase in the market share of British retail financial services of The Royal Bank of Scotland. Direct Line is a case of planned success that questions the extent to which banks’ competencies must change to master alternative delivery channels. The success of Direct Line also suggested more effective execution than other activities explored by managers of the Royal Bank of Scotland.Financial institutions; technological change; corporate strategy

Discriminatively Trained Latent Ordinal Model for Video Classification

We study the problem of video classification for facial analysis and human action recognition. We propose a novel weakly supervised learning method that models the video as a sequence of automatically mined, discriminative sub-events (eg. onset and offset phase for "smile", running and jumping for "highjump"). The proposed model is inspired by the recent works on Multiple Instance Learning and latent SVM/HCRF -- it extends such frameworks to model the ordinal aspect in the videos, approximately. We obtain consistent improvements over relevant competitive baselines on four challenging and publicly available video based facial analysis datasets for prediction of expression, clinical pain and intent in dyadic conversations and on three challenging human action datasets. We also validate the method with qualitative results and show that they largely support the intuitions behind the method.Comment: Paper accepted in IEEE TPAMI. arXiv admin note: substantial text overlap with arXiv:1604.0150