Dynamic Structures for Evolving Tactics and Strategies in Team Robotics

The autonomous robot systems of the future will be teams of robots with complementary specialisms. At any instant robot interactions determine relational structures, and sequences of these structures describe the team dynamics as trajectories through space and time. These structures can be represented in algebraic forms that are realizable as dynamic multilevel data structures within individual robots, as the basis of emergent team data structures. Such formalisms are necessary for robots to learn new individual and collective behaviours. The theory is illustrated by the example of robot soccer where robot interactions create structures and trajectories essential to the evolution of new tactics and strategies in a changing environment

Non-native contrasts in Tongan loans

We present three case studies of marginal contrasts in Tongan loans from English, working with data from three speakers. Although Tongan lacks contrasts in stress or in CC vs. CVC sequences, secondary stress in loans is contrastive, and is sensitive to whether a vowel has a correspondent in the English source word; vowel deletion is also sensitive to whether a vowel is epenthetic as compared to the English source; and final vowel length is sensitive to whether the penultimate vowel is epenthetic, and if not, whether it corresponds to a stressed or unstressed vowel in the English source. We provide an analysis in the multilevel model of Boersma (1998) and Boersma & Hamann (2009), and show that the loan patterns can be captured using only constraints that plausibly are needed for native-word phonology, including constraints that reflect perceptual strategies

Large-Scale Neural Systems for Vision and Cognition

— Consideration of how people respond to the question What is this? has suggested new problem frontiers for pattern recognition and information fusion, as well as neural systems that embody the cognitive transformation of declarative information into relational knowledge. In contrast to traditional classification methods, which aim to find the single correct label for each exemplar (This is a car), the new approach discovers rules that embody coherent relationships among labels which would otherwise appear contradictory to a learning system (This is a car, that is a vehicle, over there is a sedan). This talk will describe how an individual who experiences exemplars in real time, with each exemplar trained on at most one category label, can autonomously discover a hierarchy of cognitive rules, thereby converting local information into global knowledge. Computational examples are based on the observation that sensors working at different times, locations, and spatial scales, and experts with different goals, languages, and situations, may produce apparently inconsistent image labels, which are reconciled by implicit underlying relationships that the network’s learning process discovers. The ARTMAP information fusion system can, moreover, integrate multiple separate knowledge hierarchies, by fusing independent domains into a unified structure. In the process, the system discovers cross-domain rules, inferring multilevel relationships among groups of output classes, without any supervised labeling of these relationships. In order to self-organize its expert system, the ARTMAP information fusion network features distributed code representations which exploit the model’s intrinsic capacity for one-to-many learning (This is a car and a vehicle and a sedan) as well as many-to-one learning (Each of those vehicles is a car). Fusion system software, testbed datasets, and articles are available from http://cns.bu.edu/techlab.Defense Advanced Research Projects Research Agency (Hewlett-Packard Company, DARPA HR0011-09-3-0001; HRL Laboratories LLC subcontract 801881-BS under prime contract HR0011-09-C-0011); Science of Learning Centers program of the National Science Foundation (SBE-0354378

How to Measure Group Selection in Real-world Populations

Multilevel selection and the evolution of cooperation are fundamental to the formation of higher-level organisation and the evolution of biocomplexity, but such notions are controversial and poorly understood in natural populations. The theoretic principles of group selection are well developed in idealised models where a population is neatly divided into multiple semi-isolated sub-populations. But since such models can be explained by individual selection given the localised frequency-dependent effects involved, some argue that the group selection concepts offered are, even in the idealised case, redundant and that in natural conditions where groups are not well-defined that a group selection framework is entirely inapplicable. This does not necessarily mean, however, that a natural population is not subject to some interesting localised frequency-dependent effects – but how could we formally quantify this under realistic conditions? Here we focus on the presence of a Simpson’s Paradox where, although the local proportion of cooperators decreases at all locations, the global proportion of cooperators increases. We illustrate this principle in a simple individual-based model of bacterial biofilm growth and discuss various complicating factors in moving from theory to practice of measuring group selection

Generating realistic scaled complex networks

Research on generative models is a central project in the emerging field of network science, and it studies how statistical patterns found in real networks could be generated by formal rules. Output from these generative models is then the basis for designing and evaluating computational methods on networks, and for verification and simulation studies. During the last two decades, a variety of models has been proposed with an ultimate goal of achieving comprehensive realism for the generated networks. In this study, we (a) introduce a new generator, termed ReCoN; (b) explore how ReCoN and some existing models can be fitted to an original network to produce a structurally similar replica, (c) use ReCoN to produce networks much larger than the original exemplar, and finally (d) discuss open problems and promising research directions. In a comparative experimental study, we find that ReCoN is often superior to many other state-of-the-art network generation methods. We argue that ReCoN is a scalable and effective tool for modeling a given network while preserving important properties at both micro- and macroscopic scales, and for scaling the exemplar data by orders of magnitude in size.Comment: 26 pages, 13 figures, extended version, a preliminary version of the paper was presented at the 5th International Workshop on Complex Networks and their Application

Team Learning, Development, and Adaptation

[Excerpt] Our purpose is to explore conceptually these themes centered on team learning, development, and adaptation. We note at the onset that this chapter is not a comprehensive review of the literature. Indeed, solid conceptual and empirical work on these themes are sparse relative to the vast amount of work on team effectiveness more generally, and therefore a thematic set of topics that are ripe for conceptual development and integration. We draw on an ongoing stream of theory development and research in these areas to integrate and sculpt a distinct perspective on team learning, development, and adaptation

Experimental Design and Data Analysis in Computer Simulation Studies in the Behavioral Sciences

Treating computer simulation studies as statistical sampling experiments subject to established principles of experimental design and data analysis should further enhance their ability to inform statistical practice and a program of statistical research. Latin hypercube designs to enhance generalizability and meta-analytic methods to analyze simulation results are presented

A multilevel neo-institutional analysis of infection prevention and control in English hospitals: coerced safety culture change?

Despite committed policy, regulative and professional efforts on healthcare safety, little is known about how such macro-interventions permeate organisations and shape culture over time. Informed by neo-institutional theory, we examined how inter-organisational influences shaped safety practices and inter-subjective meanings following efforts for coerced culture change. We traced macro-influences from 2000 to 2015 in infection prevention and control (IPC). Safety perceptions and meanings were inductively analysed from 130 in-depth qualitative interviews with senior- and middle-level managers from 30 English hospitals. A total of 869 institutional interventions were identified; 69% had a regulative component. In this context of forced implementation of safety practices, staff experienced inherent tensions concerning the scope of safety, their ability to be open and prioritisation of external mandates over local need. These tensions stemmed from conflicts among three co-existing institutional logics prevalent in the NHS. In response to requests for change, staff flexibly drew from a repertoire of cognitive, material and symbolic resources within and outside their organisations. They crafted 'strategies of action', guided by a situated assessment of first-hand practice experiences complementing collective evaluations of interventions such as 'pragmatic', 'sensible' and also 'legitimate'. Macro-institutional forces exerted influence either directly on individuals or indirectly by enriching the organisational cultural repertoire