Intrusiveness, Trust and Argumentation: Using Automated Negotiation to Inhibit the Transmission of Disruptive Information

The question of how to promote the growth and diffusion of information has been extensively addressed by a wide research community. A common assumption underpinning most studies is that the information to be transmitted is useful and of high quality. In this paper, we endorse a complementary perspective. We investigate how the growth and diffusion of high quality information can be managed and maximized by preventing, dampening and minimizing the diffusion of low quality, unwanted information. To this end, we focus on the conflict between pervasive computing environments and the joint activities undertaken in parallel local social contexts. When technologies for distributed activities (e.g. mobile technology) develop, both artifacts and services that enable people to participate in non-local contexts are likely to intrude on local situations. As a mechanism for minimizing the intrusion of the technology, we develop a computational model of argumentation-based negotiation among autonomous agents. A key component in the model is played by trust: what arguments are used and how they are evaluated depend on how trustworthy the agents judge one another. To gain an insight into the implications of the model, we conduct a number of virtual experiments. Results enable us to explore how intrusiveness is affected by trust, the negotiation network and the agents' abilities of conducting argumentation

The Shell Model, the Renormalization Group and the Two-Body Interaction

The no-core shell model and the effective interaction $V_{{\rm low} k}$ can both be derived using the Lee-Suzuki projection operator formalism. The main difference between the two is the choice of basis states that define the model space. The effective interaction $V_{{\rm low} k}$ can also be derived using the renormalization group. That renormalization group derivation can be extended in a straight forward manner to also include the no-core shell model. In the nuclear matter limit the no-core shell model effective interaction in the two-body approximation reduces identically to $V_{{\rm low} k}$. The same considerations apply to the Bloch-Horowitz version of the shell model and the renormalization group treatment of two-body scattering by Birse, McGovern and Richardson

Coalition Formation with Spatial and Temporal Constraints

The coordination of emergency responders and robots to undertake a number of tasks in disaster scenarios is a grand challenge for multi-agent systems. Central to this endeavour is the problem of forming the best teams (coalitions) of responders to perform the various tasks in the area where the disaster has struck. Moreover, these teams may have to form, disband, and reform in different areas of the disaster region. This is because in most cases there will be more tasks than agents. Hence, agents need to schedule themselves to attempt each task in turn. Second, the tasks themselves can be very complex: requiring the agents to work on them for different lengths of time and having deadlines by when they need to be completed. The problem is complicated still further when different coalitions perform tasks with different levels of efficiency. Given all these facets, we define this as The Coalition Formation with Spatial and Temporal constraints problem (CFSTP).We show that this problem is NP-hard—in particular, it contains the wellknown complex combinatorial problem of Team Orienteering as a special case. Based on this, we design a Mixed Integer Program to optimally solve small-scale instances of the CFSTP and develop new anytime heuristics that can, on average, complete 97% of the tasks for large problems (20 agents and 300 tasks). In so doing, our solutions represent the first results for CFSTP

On Similarities between Inference in Game Theory and Machine Learning

In this paper, we elucidate the equivalence between inference in game theory and machine learning. Our aim in so doing is to establish an equivalent vocabulary between the two domains so as to facilitate developments at the intersection of both fields, and as proof of the usefulness of this approach, we use recent developments in each field to make useful improvements to the other. More specifically, we consider the analogies between smooth best responses in fictitious play and Bayesian inference methods. Initially, we use these insights to develop and demonstrate an improved algorithm for learning in games based on probabilistic moderation. That is, by integrating over the distribution of opponent strategies (a Bayesian approach within machine learning) rather than taking a simple empirical average (the approach used in standard fictitious play) we derive a novel moderated fictitious play algorithm and show that it is more likely than standard fictitious play to converge to a payoff-dominant but risk-dominated Nash equilibrium in a simple coordination game. Furthermore we consider the converse case, and show how insights from game theory can be used to derive two improved mean field variational learning algorithms. We first show that the standard update rule of mean field variational learning is analogous to a Cournot adjustment within game theory. By analogy with fictitious play, we then suggest an improved update rule, and show that this results in fictitious variational play, an improved mean field variational learning algorithm that exhibits better convergence in highly or strongly connected graphical models. Second, we use a recent advance in fictitious play, namely dynamic fictitious play, to derive a derivative action variational learning algorithm, that exhibits superior convergence properties on a canonical machine learning problem (clustering a mixture distribution)

Behaviour change at work: Empowering energy efficiency in the workplace through user-centred design

Copyright @ 2011 University of California eScholarship RepositoryCO2 emissions from non-domestic buildings - primarily workplaces - make up 18 percent of the UK's carbon footprint. A combination of technology advances and behavioural changes have the potential to make significant impact, but interventions have often been planned in ways which do not take into account the needs, levels of understanding and everyday behavioural contexts of building users - and hence do not achieve the hoped-for success.This paper provides a brief introduction to the Empower project, a current industrial-academic collaboration in the UK which is applying methods from user-centred design practice to understand diverse users' needs, priorities, mental models of energy and decision-making heuristics - as well as the affordances available to them - in a number of office buildings. We are developing and trialling a set of low-cost, simple software interventions tailored to multiple user groups with different degrees of agency over their energy use, which seek to influence more energy efficient behaviour at work in areas such as HVAC, lighting and equipment use. The project comprises an ethnographic research phase, a participatory design programme involving building users in the design of interventions, and iterative trials in a large office building in central London

Human-agent collectives

We live in a world where a host of computer systems, distributed throughout our physical and information environments, are increasingly implicated in our everyday actions. Computer technologies impact all aspects of our lives and our relationship with the digital has fundamentally altered as computers have moved out of the workplace and away from the desktop. Networked computers, tablets, phones and personal devices are now commonplace, as are an increasingly diverse set of digital devices built into the world around us. Data and information is generated at unprecedented speeds and volumes from an increasingly diverse range of sources. It is then combined in unforeseen ways, limited only by human imagination. People’s activities and collaborations are becoming ever more dependent upon and intertwined with this ubiquitous information substrate. As these trends continue apace, it is becoming apparent that many endeavours involve the symbiotic interleaving of humans and computers. Moreover, the emergence of these close-knit partnerships is inducing profound change. Rather than issuing instructions to passive machines that wait until they are asked before doing anything, we will work in tandem with highly inter-connected computational components that act autonomously and intelligently (aka agents). As a consequence, greater attention needs to be given to the balance of control between people and machines. In many situations, humans will be in charge and agents will predominantly act in a supporting role. In other cases, however, the agents will be in control and humans will play the supporting role. We term this emerging class of systems human-agent collectives (HACs) to reflect the close partnership and the flexible social interactions between the humans and the computers. As well as exhibiting increased autonomy, such systems will be inherently open and social. This means the participants will need to continually and flexibly establish and manage a range of social relationships. Thus, depending on the task at hand, different constellations of people, resources, and information will need to come together, operate in a coordinated fashion, and then disband. The openness and presence of many distinct stakeholders means participation will be motivated by a broad range of incentives rather than diktat. This article outlines the key research challenges involved in developing a comprehensive understanding of HACs. To illuminate this agenda, a nascent application in the domain of disaster response is presented

A MOOC for Adult Learners of Mathematics and Statistics: Tensions and Compromises in Design

There are many adults with low mathematical/statistical knowledge who would like to enhance that understanding. There are insufficient teachers to respond to the level of need and so innovative solutions must be found. In the UK, the Ufi Charitable Trust has funded a project to develop a free open online course to offer motivated adults access to powerful ideas. We reflect on the tensions and compromises that emerged during its design. More specifically, referring to data collected from users, we consider the challenge of developing resources that will support heterogeneous students from unknown backgrounds, who may have already been failed by the conventional educational system and who will have no interactive tutor support within this course

Mapping the co-benefits of climate change action to issues of public concern in the UK: a narrative review

To avoid a 1·5°C rise in global temperatures above preindustrial levels, the next phase of reductions in greenhouse gas emissions will need to be comparatively rapid. Linking the co-benefits of climate action to wider issues that the public are concerned about can help decision makers to prioritise decarbonisation options that increase the chance of public support for such changes, while ensuring that a just transition is delivered. We identified key issues of concern to the UK public by use of Ipsos MORI public opinion data from 2007 to 2020 and used these data to guide a narrative review of academic and grey literature on the co-benefits of climate change action for the UK. Correspondence with civil servants, third sector organisations, and relevant academics allowed us to identify omissions and to ensure policy relevance of the recommendations. This evidence-based Review of the various co-benefits of climate change action for the UK identifies four main areas: health and the National Health Service; security; economy and unemployment; and poverty, housing, and inequality. Associated trade-offs are also discussed. City-level and regional-level governments are particularly well placed to incorporate co-benefits into their decision making because it is at this scale that co-benefits most clearly manifest, and where interventions can have the most immediate effects

Growth inhibition of cytosolic Salmonella by caspase-1 and caspase-11 precedes host cell death

Sensing bacterial products in the cytosol of mammalian cells by NOD-like receptors leads to the activation of caspase-1 inflammasomes, and the production of the pro-inflammatory cytokines interleukin (IL)-18 and IL-1β. In addition, mouse caspase-11 (represented in humans by its orthologs, caspase-4 and caspase-5) detects cytosolic bacterial LPS directly. Activation of caspase-1 and caspase-11 initiates pyroptotic host cell death that releases potentially harmful bacteria from the nutrient-rich host cell cytosol into the extracellular environment. Here we use single cell analysis and time-lapse microscopy to identify a subpopulation of host cells, in which growth of cytosolic Salmonella Typhimurium is inhibited independently or prior to the onset of cell death. The enzymatic activities of caspase-1 and caspase-11 are required for growth inhibition in different cell types. Our results reveal that these proteases have important functions beyond the direct induction of pyroptosis and proinflammatory cytokine secretion in the control of growth and elimination of cytosolic bacteria