Equipping Simulation Model (BIOSIM)’s Actors With Multi-agent Intelligence on Cross platforms

Thesis is to Introduce an Intelligent cross platform architecture with Multi-agent system in order to equip the simulation Models with agents, having intelligent behavior, reactive and pro-active nature and rational in decision making.

Color detection and segmentation for road and traffic signs

This paper aims to present three new methods for color detection and segmentation of road signs. The images are taken by a digital camera mounted in a car. The RGB images are converted into IHLS color space, and new methods are applied to extract the colors of the road signs under consideration. The methods are tested on hundreds of outdoor images in different light conditions, and they show high robustness. This project is part of the research taking place in Dalarna University / Sweden in the field of the ITS.

Tourism and Arctic Observation Systems : exploring the relationships

The Arctic is affected by global environmental change and also by diverse interests from many economic sectors and industries. Over the last decade, various actors have attempted to explore the options for setting up integrated and comprehensive trans-boundary systems for monitoring and observing these impacts. These Arctic Observation Systems (AOS) contribute to the planning, implementation, monitoring and evaluation of environmental change and responsible social and economic development in the Arctic. The aim of this article is to identify the two-way relationship between AOS and tourism. On the one hand, tourism activities account for diverse changes across a broad spectrum of impact fields. On the other hand, due to its multiple and diverse agents and far-reaching activities, tourism is also well-positioned to collect observational data and participate as an actor in monitoring activities. To accomplish our goals, we provide an inventory of tourism-embedded issues and concerns of interest to AOS from a range of destinations in the circumpolar Arctic region, including Alaska, Arctic Canada, Iceland, Svalbard, the mainland European Arctic and Russia. The article also draws comparisons with the situation in Antarctica. On the basis of a collective analysis provided by members of the International Polar Tourism Research Network from across the polar regions, we conclude that the potential role for tourism in the development and implementation of AOS is significant and has been overlooked.

Fasting activates the gene expression of UCP3 independent of genes necessary for lipid transport and oxidation in skeletal muscle

Fasting triggers a complex array of adaptive metabolic and hormonal responses including an augmentation in the capacity for mitochondrial fatty acid (FA) oxidation in skeletal muscle. This study hypothesized that this adaptive response is mediated by increased mRNA of key genes central to the regulation of fat oxidation in human skeletal muscle. Fasting dramatically increased UCP3 gene expression, by 5-fold at 15 h and 10-fold at 40 h. However the expression of key genes responsible for the uptake, transport, oxidation, and re-esterification of FA remained unchanged following 15 and 40 h of fasting. Likewise there was no change in the mRNA abundance of transcription factors. This suggests a unique role for UCP3 in the regulation of FA homeostasis during fasting as adaptation to 40 h of fasting does not require alterations in the expression of other genes necessary for lipid metabolism.

Learning to minimize energy costs and maximize mechanical work in a bimanual coordination task

The authors addressed the hypothesis that economy in motor coordination is a learning phenomenon realized by both reduced energy cost for a given workload and more external work at the same prepractice metabolic and attentional energy expenditure. "Self-optimization" of movement parameters has been proposed to reflect learned motor adaptations that minimize energy costs. Twelve men aged 22.3 [+ or -] 3.9 years practiced a 90[degrees] relative phase, upper limb, independent ergometer cycling task at 60 rpm, followed by a transfer test of unpracticed (45 and 75 rpm) and self-paced cadences. Performance in all conditions was initially unstable, inaccurate, and relatively high in both metabolic and attentional energy costs. With practice, coordinative stability increased, more work was performed for the same metabolic and attentional costs, and the same work was done at a reduced energy cost. Self-paced cycling was initially below the metabolically optimal, but following practice at 60 rpm was closer to optimal cadence. Given the many behavioral options of the motor system in meeting a variety of everyday movement task goals, optimal metabolic and attentional energy criteria may provide a solution to the problem of selecting the most adaptive coordination and control parameters.

Coalition Formation through Motivation and Trust

Cooperation is the fundamental underpinning of multi-agent systems, allowing agents to interact to achieve their goals. Where agents are self-interested, or potentially unreliable, there must be appropriate mechanisms to cope with the uncertainty that arises. In particular, agents must manage the risk associated with interacting with others who have different objectives, or who may fail to fulfil their commitments. Previous work has utilised the notions of motivation and trust in engendering successful cooperation between self-interested agents. Motivations provide a means for representing and reasoning about agents' overall objectives, and trust offers a mechanism for modelling and reasoning about reliability, honesty, veracity and so forth. This paper extends that work to address some of its limitations. In particular, we introduce the concept of a clan: a group of agents who trust each other and have similar objectives. Clan members treat each other favourably when making private decisions about cooperation, in order to gain mutual benefit. We describe mechanisms for agents to form, maintain, and dissolve clans in accordance with their self-interested nature, along with giving details of how clan membership influences individual decision making. Finally, through some simulation experiments we illustrate the effectiveness of clan formation in addressing some of the inherent problems with cooperation among self-interested agents.

Sociological Agents for Effective Social Action

This paper is concerned with the problem of how effective social interaction arises from individual social action and mind. The need to study the individual social mind, suggests a move towards the notion of sociological agents who can model their social environment as opposed to acting socially within it. This does not constrain such social behaviour; on the contrary, we argue that it provides the requisite information and understanding for such behaviour to be effective. We argue that effective social agents must be sociological in modelling agents and agent relationships. In this paper, we show how an existing agent framework leads naturally to the enumeration of a map of inter-agent relationships that can be modelled and exploited by sociological agents to enable more effective operation.

Monitoring and Explanation of Contract Execution: A Case Study in the Aerospace Domain

In the domain of aerospace aftermarkets, which often has long supply chains that feed into the maintenance of aircraft, contracts are used to establish agreements between aircraft operators and maintenance suppliers. However, violations at the bottom of the supply chain (part suppliers) can easily cascade to the top (aircraft operators), making it difficult to determine the source of the violation, and seek to address it. In this context, we have developed a global monitoring architecture that ensures the detection of norm violations and generates explanations for the origin of violations. In this paper, we describe the implementation and deployment of a global monitor in the aerospace domain of [8] and show how it generates explanations for violations within the maintenance supply chain. We show how these explanations can be used not only to detect violations at runtime, but also to uncover potential problems in contracts before their deployment, thus improving them.

Norm-based behaviour modification in BDI agents

While there has been much work on developing frameworks and models of norms and normative systems, consideration of the impact of norms on the practical reasoning of agents has attracted less attention. The problem is that traditional agent architectures and their associated languages provide no mechanism to adapt an agent at runtime to norms constraining their behaviour. This is important because if BDI-type agents are to operate in open environments, they need to adapt to changes in the norms that regulate such environments. In response, in this paper we provide a technique to extend BDI agent languages, by enabling them to enact behaviour modification at runtime in response to newly accepted norms. Our solution consists of creating new plans to comply with obligations and suppressing the execution of existing plans that violate prohibitions. We demonstrate the viability of our approach through an implementation of our solution in the AgentSpeak(L) language.

Evolutionary Testing of Autonomous Software Agents

A system built in terms of autonomous agents may require even greater correctness assurance than one which is merely reacting to the immediate control of its users. Agents make substantial decisions for themselves, so thorough testing is an important consideration. However, autonomy also makes testing harder; by their nature, autonomous agents may react in different ways to the same inputs over time, because, for instance they have changeable goals and knowledge. For this reason, we argue that testing of autonomous agents requires a procedure that caters for a wide range of test case contexts, and that can search for the most demanding of these test cases, even when they are not apparent to the agents’ developers. In this paper, we address this problem, introducing and evaluating an approach to testing autonomous agents that uses evolutionary optimization to generate demanding test cases.