395,547 research outputs found

    A multi-agent-based evolution model of innovation networks in dynamic environments

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    An innovation network can be considered as a complex adaptive system with evolution affected by dynamic environments. This paper establishes a multi-agent-based evolution model of innovation networks under dynamic settings through computational and logical modeling, and a multi-agent system paradigm. This evolution model is composed of several sub-models of agents' knowledge production by independent innovations in dynamic situations, knowledge learning by cooperative innovations covering agents' heterogeneities, decision-making for innovation selections, and knowledge update considering decay factors. On the basis of above-mentioned sub-models, an evolution rule for multi-agent based innovation network system is given. The proposed evolution model can be utilized to simulate and analyze different scenarios of innovation networks in various dynamic environments and support decision-making for innovation network optimization

    Real-time Spatial Detection and Tracking of Resources in a Construction Environment

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    Construction accidents with heavy equipment and bad decision making can be based on poor knowledge of the site environment and in both cases may lead to work interruptions and costly delays. Supporting the construction environment with real-time generated three-dimensional (3D) models can help preventing accidents as well as support management by modeling infrastructure assets in 3D. Such models can be integrated in the path planning of construction equipment operations for obstacle avoidance or in a 4D model that simulates construction processes. Detecting and guiding resources, such as personnel, machines and materials in and to the right place on time requires methods and technologies supplying information in real-time. This paper presents research in real-time 3D laser scanning and modeling using high range frame update rate scanning technology. Existing and emerging sensors and techniques in three-dimensional modeling are explained. The presented research successfully developed computational models and algorithms for the real-time detection, tracking, and three-dimensional modeling of static and dynamic construction resources, such as workforce, machines, equipment, and materials based on a 3D video range camera. In particular, the proposed algorithm for rapidly modeling three-dimensional scenes is explained. Laboratory and outdoor field experiments that were conducted to validate the algorithm’s performance and results are discussed

    Acting and Modeling the Future of Dams: Knowledge Production Processes in Sustainability Science

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    Sustainability scientists are developing new knowledge production processes (KPPs) based on findings that science has a greater impact on decision-making when it (1) adopts an interdisciplinary systems approach, and (2) is participatory and, therefore, perceived as more salient, legitimate, and credible by users. This presentation will discuss the findings from a review of the literature on the intersection of two KPP methods: systems dynamics (SD) and role-play simulations (RPS). SD is a powerful approach for modeling dynamic, complex systems to improve understanding of system behaviors in coupled social-ecological systems. It can capture complex biophysical phenomena and trade-offs, while also representing feedbacks and thresholds from social and institutional systems. It incorporates both qualitative and quantitative information. Unlike static models, SD is explicitly dynamic. It is well suited to group modeling efforts and informing consensus-based decisions. RPSs are experiential, scenario-based tools that help participants learn about how science is used in policy-making decisions, learn about others\u27 preferences and priorities regarding a public policy decision, develop and evaluate innovative options for addressing critical challenges, and contribute to building consensus among diverse and interdependent stakeholders. Although both approaches aim to improve the basis for decision-making, they are rarely discussed together. This presentation considers the literature on each method and their intersection by analyzing: (1) each method\u27s objectives and functions, (2) the steps in their processes for incorporating participation and interdisciplinary, systems-based knowledge, (3) approaches for evaluating outcomes, (4) strengths and weaknesses, (5) opportunities and challenges for integrations, and identifies recommendations for future research. A version of the presentation with an attached transcript can be found here

    A Cognitive Modeling Approach to Strategy Formation in Dynamic Decision Making

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    Decision-making is a high-level cognitive process based on cognitive processes like perception, attention, and memory. Real-life situations require series of decisions to be made, with each decision depending on previous feedback from a potentially changing environment. To gain a better understanding of the underlying processes of dynamic decision-making, we applied the method of cognitive modeling on a complex rule-based category learning task. Here, participants first needed to identify the conjunction of two rules that defined a target category and later adapt to a reversal of feedback contingencies. We developed an ACT-R model for the core aspects of this dynamic decision-making task. An important aim of our model was that it provides a general account of how such tasks are solved and, with minor changes, is applicable to other stimulus materials. The model was implemented as a mixture of an exemplar-based and a rule-based approach which incorporates perceptual-motor and metacognitive aspects as well. The model solves the categorization task by first trying out one-feature strategies and then, as a result of repeated negative feedback, switching to two-feature strategies. Overall, this model solves the task in a similar way as participants do, including generally successful initial learning as well as reversal learning after the change of feedback contingencies. Moreover, the fact that not all participants were successful in the two learning phases is also reflected in the modeling data. However, we found a larger variance and a lower overall performance of the modeling data as compared to the human data which may relate to perceptual preferences or additional knowledge and rules applied by the participants. In a next step, these aspects could be implemented in the model for a better overall fit. In view of the large interindividual differences in decision performance between participants, additional information about the underlying cognitive processes from behavioral, psychobiological and neurophysiological data may help to optimize future applications of this model such that it can be transferred to other domains of comparable dynamic decision tasks.DFG, 54371073, SFB/TRR 62: Eine Companion-Technologie für kognitive technische System

    Planning to ‘Hear the Farmer’s Voice’: an Agent-Based Modelling Approach to Agricultural Land Use Planning

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    Agricultural land use is influenced not only by multiple aspects of biophysical and socio-economic processes, but also the cumulative impacts of individual farmer decisions. Farmers’ activities and decisions at farm scale shape land use and water utilisation at regional scale, yet land use planning processes do not take into account farmers’ knowledge and decision-making processes as they respond to, and in turn shape, change. Farmers’ voices are missing in the planning system. In this paper, we address the complexity of agricultural land use planning and examine the possibility of agricultural land use planning from the bottom-up via simulation to integrate environmental, economic and human factors that influence land use change. We present an innovative approach to model the interactions between government policy, market signals, and farmers’ land use decisions, and how the accumulated effects of these individual decisions change agricultural land use patterns at regional scale, using spatial and temporal agent-based modeling. A multi-stage mixed method spatial agent-based modeling (ABM) approach, aligned with the Geodesign framework, can incorporate local knowledge and decision-making into models of regional land use change. To illustrate the new approach, we examine the impact of milk market price on changes in land use in Tasmania, Australia. This approach brings together local knowledge with scientific, planning, and policy knowledge to generate dynamic scenarios for informed agricultural land-use planning decisions

    Agent-based hybrid framework for decision making on complex problems

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    Electronic commerce and the Internet have created demand for automated systems that can make complex decisions utilizing information from multiple sources. Because the information is uncertain, dynamic, distributed, and heterogeneous in nature, these systems require a great diversity of intelligent techniques including expert systems, fuzzy logic, neural networks, and genetic algorithms. However, in complex decision making, many different components or sub-tasks are involved, each of which requires different types of processing. Thus multiple such techniques are required resulting in systems called hybrid intelligent systems. That is, hybrid solutions are crucial for complex problem solving and decision making. There is a growing demand for these systems in many areas including financial investment planning, engineering design, medical diagnosis, and cognitive simulation. However, the design and development of these systems is difficult because they have a large number of parts or components that have many interactions. From a multi-agent perspective, agents in multi-agent systems (MAS) are autonomous and can engage in flexible, high-level interactions. MASs are good at complex, dynamic interactions. Thus a multi-agent perspective is suitable for modeling, design, and construction of hybrid intelligent systems. The aim of this thesis is to develop an agent-based framework for constructing hybrid intelligent systems which are mainly used for complex problem solving and decision making. Existing software development techniques (typically, object-oriented) are inadequate for modeling agent-based hybrid intelligent systems. There is a fundamental mismatch between the concepts used by object-oriented developers and the agent-oriented view. Although there are some agent-oriented methodologies such as the Gaia methodology, there is still no specifically tailored methodology available for analyzing and designing agent-based hybrid intelligent systems. To this end, a methodology is proposed, which is specifically tailored to the analysis and design of agent-based hybrid intelligent systems. The methodology consists of six models - role model, interaction model, agent model, skill model, knowledge model, and organizational model. This methodology differs from other agent-oriented methodologies in its skill and knowledge models. As good decisions and problem solutions are mainly based on adequate information, rich knowledge, and appropriate skills to use knowledge and information, these two models are of paramount importance in modeling complex problem solving and decision making. Follow the methodology, an agent-based framework for hybrid intelligent system construction used in complex problem solving and decision making was developed. The framework has several crucial characteristics that differentiate this research from others. Four important issues relating to the framework are also investigated. These cover the building of an ontology for financial investment, matchmaking in middle agents, reasoning in problem solving and decision making, and decision aggregation in MASs. The thesis demonstrates how to build a domain-specific ontology and how to access it in a MAS by building a financial ontology. It is argued that the practical performance of service provider agents has a significant impact on the matchmaking outcomes of middle agents. It is proposed to consider service provider agents\u27 track records in matchmaking. A way to provide initial values for the track records of service provider agents is also suggested. The concept of ‘reasoning with multimedia information’ is introduced, and reasoning with still image information using symbolic projection theory is proposed. How to choose suitable aggregation operations is demonstrated through financial investment application and three approaches are proposed - the stationary agent approach, the token-passing approach, and the mobile agent approach to implementing decision aggregation in MASs. Based on the framework, a prototype was built and applied to financial investment planning. This prototype consists of one serving agent, one interface agent, one decision aggregation agent, one planning agent, four decision making agents, and five service provider agents. Experiments were conducted on the prototype. The experimental results show the framework is flexible, robust, and fully workable. All agents derived from the methodology exhibit their behaviors correctly as specified

    Dynamic impact modeling as a road transport crisis management support tool

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    Crisis management must provide data to allow for real-time decision-making. Accurate data is especially needed to minimize the risk of critical infrastructure failure. Research into the possible impacts of critical infrastructure failure is a part of developing a functional and secure infrastructure for each nation state. Road transport is one such sector that has a significant impact on its functions. When this fails, there may be a cascading spread of impacts on the energy, health, and other sectors. In this regard, this paper focuses on the dynamic modeling of the impacts of critical road infrastructure failures. It proposes a dynamic modeling system based on a stochastic approach. Its essence is the macroscopic model-based comparative analysis of a road with a critical element and detour roads. The outputs of this system are planning documents that determine the impacts of functional parameter degradation on detour roads-not only applicable in decision-making concerning the selection of the optimal detour road, but also as a support mechanism in minimising possible risks. In this article we aim to expand the extent of knowledge in the Crisis management and critical infrastructure protection in the road transport sector fields.Ministry of the Interior of the Czech Republic [VI20152019049]; Technology Agency of the Czech Republic [TE01020168]; VSB-Technical University of Ostrava [SP2019/96]Ministerstvo Vnitra České Republiky: VI20152019049; Technology Agency of the Czech Republic, TACR: TE01020168; Vysoká Škola Bánská - Technická Univerzita Ostrava: SP2019/9
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