1,721,142 research outputs found
Analysis of DNA based measurement methods applied to human chromosome classification
No full textApplied Science
Traffic Light Control by Multiagent Reinforcement Learning Systems
No full textTraffic light control is one of the main means of controlling road traffic. Improving traffic control is important because it can lead to higher traffic throughput and reduced traffic congestion. This chapter describes multiagent reinforcement learning techniques for automatic optimization of traffic light controllers. Such techniques are attractive because they can automatically discover efficient control strategies for complex tasks, such as traffic control, for which it is hard or impossible to compute optimal solutions directly and hard to develop hand-coded solutions. First, the general multi-agent reinforcement learning framework is described, which is used to control traffic lights in this work. In this framework, multiple local controllers (agents) are each responsible for the optimization of traffic lights around a single traffic junction, making use of locally perceived traffic state information (sensed cars on the road), a learned probabilistic model of car behavior, and a learned value function which indicates how traffic light decisions affect longterm utility, in terms of the average waiting time of cars. Next, three extensions are described which improve upon the basic framework in various ways: agents (traffic junction controllers) taking into account congestion information from neighboring agents; handling partial observability of traffic states; and coordinating the behavior of multiple agents by coordination graphs and the max-plus algorithm. © 2010 Springer-Verlag Berlin Heidelberg
Interacting with adaptive systems
No full textThis chapter concerns user responses toward adaptive and autonomous system behavior. The work consists of a review of the relevant literature off-set with the findings from three studies that investigated the way people respond to adaptive and autonomous agents. The systems evaluated in this chapter make decisions on behalf of the user, behave autonomously and need user feedback or compliance. Apart from the need for systems to competently perform their tasks, people will see adaptive and autonomous system behavior as social actions. Factors from humans’ social interaction will therefore also play a role in the experience users will have when using the systems. The user needs to trust, understand and control the system’s autonomous actions. In some cases the users need to invest effort in training the system so that it can learn. This indicates a complex relationship between the user, the adaptive and autonomous system and the specific context in which the system is used. This chapter specifically evaluates the way people trust and understand a system as well as the effects of system transparency and autonomy
Appearance-based concurrent map building and localization using a multi-hypotheses tracker
No full textAdaptive Hierarchical Multi-agent Organizations
No full textIn this chapter, we discuss the design of adaptive hierarchical organizations for multi-agent systems (MAS). Hierarchical organizations have a number of advantages such as their ability to handle complex problems and their scalability to large organizations. By introducing adaptivity in the structure of hierarchical MAS organizations, we enable agents to balance resources in their organization. We will first provide a number of generic principles for the design of hierarchical MAS organizations. We show how these principles are used to design three different hierarchical organizations for a search and rescue task in the RoboCupRescue simulation environment. The first two of these organizations are static, and the third is able to adapt its structure. An empirical study on the performance of these three organizations shows that the dynamic organization performs better than the two static organizations
Bayesian networks for expert systems: Theory and practical applications
Get PDFContains fulltext :
94188.pdf (Author’s version preprint ) (Open Access
A Distributed Approach to Gas Detection and Source Localization Using Heterogeneous Information
No full textThis chapter introduces a system for early detection of gaseous substances and coarse source localization by using heterogeneous sensor measurements and human reports. The system is based on Distributed Perception Networks, a Multi-agent system framework implementing distributed Bayesian reasoning. Causal probabilistic models are exploited in several complementary ways. They support uniform and efficient integration of very heterogeneous information sources, such as different static and mobile sensors as well as human reports. In principle, modular Bayesian networks allow creation of complex probabilistic observation models which adapt to changing constellations of information sources at runtime. On the other hand, Bayesian networks are used also for coarse modeling of transitions in the gas propagation processes. By combining dynamic models of gas propagation processes with the observation models, we obtain adaptive Bayesian systems which correspond to Hidden Markov Models. The resulting systems facilitate seamless combination of prior domain knowledge and heterogeneous observations
A call for sensemaking support systems in crisis management
No full textIn this chapter, we explore four information processing challenges commonly experienced in crisis situations, which form the basis of the design of information systems that should support actors in these situations. When we explore the difference between Sensemaking and decision making, two activities that are undertaken to cope with information processing challenges, we can understand the two types of information systems support that are needed. The first type—decision support systems—supports actors in dealing with information-related problems of uncertainty and complexity, and is the traditional focus of information systems design. The second type—sensemaking support systems—should support actors in dealing with problems of frames of reference, ambiguity, and equivocality, but is not commonplace yet. We conducted three case studies in different crisis situations to explore these information processing challenges: A case study of the sudden crisis of an airplane crash in the Barents Rescue Exercise, a case study of the yearly recurring forest fires crises in Portugal, and a case study of the post-conflict European Union Police Mission in Bosnia and Herzegovina. We discuss design premises for crisis management information systems and compare these to our findings, and observe that systems designed accordingly will provide for the necessary Sensemaking support
- …
