The Experts below are selected from a list of 27363 Experts worldwide ranked by ideXlab platform
Samuel Holtzman - One of the best experts on this subject based on the ideXlab platform.
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r d analyst an interactive approach to normative decision system model construction
arXiv: Artificial Intelligence, 2013Co-Authors: Peter L Regan, Samuel HoltzmanAbstract:This paper describes the architecture of R&D Analyst, a commercial intelligent decision system for evaluating corporate research and development projects and portfolios. In analyzing projects, R&D Analyst interactively guides a user in constructing an Influence Diagram model for an individual research project. The system's interactive approach can be clearly explained from a blackboard system perspective. The opportunistic reasoning emphasis of blackboard systems satisfies the flexibility requirements of model construction, thereby suggesting that a similar architecture would be valuable for developing normative decision systems in other domains. Current research is aimed at extending the system architecture to explicitly consider of sequential decisions involving limited temporal, financial, and physical resources.
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r d decision advisor an interactive approach to normative decision system model construction
European Journal of Operational Research, 1995Co-Authors: Peter J Regan, Samuel HoltzmanAbstract:Abstract This paper describes the architecture of R&D Decision Advisor ™ , a commercial intelligent decision system for evaluating corporate research and development projects and portfolios. In analyzing projects, R & D Decision Advisor interactively guides a user in constructing an Influence Diagram model for an individual research project. The system's interactive approach can be clearly explained from a blackboard perspective. The system guides the user to choose among general project features but offers flexibility to capture unique project details. Related research generalizes the underlying architecture and addresses resource-constrained decision situations.
Nima Khakzad - One of the best experts on this subject based on the ideXlab platform.
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optimal firefighting to prevent domino effects methodologies based on dynamic Influence Diagram and mathematical programming
Reliability Engineering & System Safety, 2021Co-Authors: Nima KhakzadAbstract:Abstract Fire is one of the most costly accidents in process plants due to the inflicted damage and the required firefighting resources. If the firefighting resources are sufficient, firefighting will include the suppression and cooling of all the burning units and exposed units, respectively. However, when the resources are inadequate, optimal firefighting strategies to answer “which burning units to suppress first and which exposed units to cool first?” would be essential to delay the fire spread until more resources become available. The present study demonstrates the application of two decision support techniques to optimal firefighting under uncertainty and limited resources: (i) Dynamic Influence Diagram, as an extension of dynamic Bayesian network, and (ii) mathematical programming. Both techniques are illustrated to be effective in identifying optimal firefighting strategies. However, unlike the dynamic Influence Diagram, the mathematical programming is demonstrated not to suffer from an exponential growth of decision alternatives, making it a more efficient technique in the case of large process plants and complicated fire spread scenarios.
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application of bayesian network to domino effect assessment
2021Co-Authors: Nima KhakzadAbstract:Abstract High complexity and growing interdependencies of chemical and process facilities make them increasingly vulnerable to domino effects. Domino effects are time-dependent processes where not only the identification of involved units but also their temporal sequence of involvement in the chain of accidents matter. In the event of fire dominoes, especially, predicting the most probable path of fire spread, both spatially and temporally, can be of great significance in the context of fire safety and firefighting. Bayesian network and its more advanced extensions such as dynamic Bayesian network and limited memory Influence Diagram (all collectively known as Bayesian network) have proved as robust techniques for spatial and temporal modeling of domino effects, vulnerability assessment of chemical and process plants (and units) with regard to domino effects, cost-effective allocation of fire protection measures, and optimal firefighting aiming at preventing or delaying the domino effects. This chapter demonstrates via simple examples the aforementioned applications of Bayesian network in domino effect modeling and safety assessment, with a particular emphasis on fire dominoes.
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a bayesian network methodology for optimal security management of critical infrastructures
Reliability Engineering & System Safety, 2019Co-Authors: Alessio Misuri, Nima Khakzad, Genserik Reniers, Valerio CozzaniAbstract:Abstract Security management of critical infrastructures is a complex task as a great variety of technical and socio-political information is needed to realistically predict the risk of intentional malevolent acts. In the present study, a methodology based on Limited Memory Influence Diagram (LIMID) has been developed for the protection of critical infrastructures via cost-effective allocation of security measures. LIMID is an extension of Bayesian network (BN) intended for decision-making, allowing for efficient modelling of complex systems while accounting for interdependencies and interaction of system components. The probability updating feature of BN has been used to investigate the effect of vulnerabilities on adversaries’ preferences when planning attacks. Moreover, the proposed methodology has been shown to be able to identify an optimal defensive strategy given an attack through maximizing defenders’ expected utility. Despite being demonstrated via a chemical facility, the methodology can easily be tailored to a wide variety of critical infrastructures.
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a graph theoretic approach to optimal firefighting in oil terminals
Energies, 2018Co-Authors: Nima KhakzadAbstract:Effective firefighting of major fires in fuel storage plants can effectively prevent or delay fire spread (domino effect) and eventually extinguish the fire. If the number of firefighting crew and equipment is sufficient, firefighting will include the suppression of all the burning units and cooling of all the exposed units. However, when available resources are not adequate, fire brigades would need to optimally allocate their resources by answering the question “which burning units to suppress first and which exposed units to cool first?” until more resources become available from nearby industrial plants or residential communities. The present study is an attempt to answer the foregoing question by developing a graph theoretic methodology. It has been demonstrated that suppression and cooling of units with the highest out-closeness index will result in an optimum firefighting strategy. A comparison between the outcomes of the graph theoretic approach and an approach based on Influence Diagram has shown the efficiency of the graph approach.
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cost effective fire protection of chemical plants against domino effects
Reliability Engineering & System Safety, 2018Co-Authors: Nima Khakzad, Genserik Reniers, Valerio Cozzani, Gabriele Landucci, Hans J PasmanAbstract:The propagation of fire-induced domino effects in chemical plants largely depends on the primary fire scenario, on separation distances between the units, and on the presence of fire protection barriers. Passive and active safety barriers are widely employed to prevent or delay the initiation or propagation of domino effects. In the present study, a methodology has been developed based on Bayesian network to account for the impact of such safety barriers on the propagation of fire domino scenarios. The Bayesian network has been extended to a limited memory Influence Diagram in order to identify a cost-effective allocation of additional safety barriers to further mitigate the fire propagation. The application of the methodology has been demonstrated using a chemical tank farm. The results are in good agreement with the results of a graph theoretic approach developed in a previous study, proving the reliability of the developed methodology in cost-effective protection of process plants.
Genserik Reniers - One of the best experts on this subject based on the ideXlab platform.
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a bayesian network methodology for optimal security management of critical infrastructures
Reliability Engineering & System Safety, 2019Co-Authors: Alessio Misuri, Nima Khakzad, Genserik Reniers, Valerio CozzaniAbstract:Abstract Security management of critical infrastructures is a complex task as a great variety of technical and socio-political information is needed to realistically predict the risk of intentional malevolent acts. In the present study, a methodology based on Limited Memory Influence Diagram (LIMID) has been developed for the protection of critical infrastructures via cost-effective allocation of security measures. LIMID is an extension of Bayesian network (BN) intended for decision-making, allowing for efficient modelling of complex systems while accounting for interdependencies and interaction of system components. The probability updating feature of BN has been used to investigate the effect of vulnerabilities on adversaries’ preferences when planning attacks. Moreover, the proposed methodology has been shown to be able to identify an optimal defensive strategy given an attack through maximizing defenders’ expected utility. Despite being demonstrated via a chemical facility, the methodology can easily be tailored to a wide variety of critical infrastructures.
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cost effective fire protection of chemical plants against domino effects
Reliability Engineering & System Safety, 2018Co-Authors: Nima Khakzad, Genserik Reniers, Valerio Cozzani, Gabriele Landucci, Hans J PasmanAbstract:The propagation of fire-induced domino effects in chemical plants largely depends on the primary fire scenario, on separation distances between the units, and on the presence of fire protection barriers. Passive and active safety barriers are widely employed to prevent or delay the initiation or propagation of domino effects. In the present study, a methodology has been developed based on Bayesian network to account for the impact of such safety barriers on the propagation of fire domino scenarios. The Bayesian network has been extended to a limited memory Influence Diagram in order to identify a cost-effective allocation of additional safety barriers to further mitigate the fire propagation. The application of the methodology has been demonstrated using a chemical tank farm. The results are in good agreement with the results of a graph theoretic approach developed in a previous study, proving the reliability of the developed methodology in cost-effective protection of process plants.
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cost effective allocation of safety measures in chemical plants w r t land use planning
Safety Science, 2017Co-Authors: Nima Khakzad, Genserik ReniersAbstract:Abstract Land-use planning (LUP) has widely been employed as a protective safety measure in risk management of major hazard installations such as chemical plants. In the European Union countries, a majority of relevant work over the past years has been inspired by the Seveso II Directive. The inclusion of LUP in the Seveso II Directive has been with the aim of mitigating off-site damage of major accidents on public via setting criteria for (i) the identification of the location and layout of new installations, (ii) the development of existing installations, and (iii) the land developments in the vicinity of existing installations. We, in the present study, have proposed a methodology based on Bayesian network (BN) for cost-effective allocation of safety measures in chemical plants so that both internal and external risks could effectively be mitigated, particularly in compliance with the requirements of LUP. We first employed BN to calculate risks, and then extended the BN to a limited memory Influence Diagram using additional decision and utility nodes so that it can be used for multi-attribute decision analysis. The development and application of the methodology have been illustrated via fireproofing of a hypothetical fuel storage plant.
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an approach for reconciling different perspectives and stakeholder views on risk ranking
Journal of Cleaner Production, 2017Co-Authors: Floris Goerlandt, Genserik ReniersAbstract:Abstract Risk ranking is a widely used technique for selecting and prioritizing multiple risks facing an organization or decision maker. In industrial practice and in academic work, risk ranking methods are commonly based an implicit risk conceptualization, adopting a particular foundational perspective and accounting for selected factors. Nevertheless, different stakeholders may adhere to various risk conceptualizations, applying different foundational perspectives and accounting for different factors in the ranking process. Identifying a gap in the literature to coherently consider these different foundations, this paper presents an approach for reconciling different foundational perspectives and stakeholder views, accounting for uncertainties about these. The approach is presented based on three foundational perspectives (the expected value, uncertainty and moral perspective), and developed as a Bayesian Network – Influence Diagram. The main use of the model is to make possible controversies in risk prioritization explicit, facilitating stakeholder deliberation. The model can easily be extended with new perspectives on risk ranking, or adapted based on additional knowledge about other aspects, e.g. relating to risk perception. The graphical representation allows insights into the contribution of various aspects, facilitating the selection of appropriate risk management actions. The developed model is applied to an example, suggesting that the envisaged benefits of the proposed approach are plausible. It is concluded that the novel approach has a clear potential for reconciling different foundational perspectives and stakeholder views on risk ranking. Nevertheless, it is stressed that the conceptual contribution made in this paper should be more elaborately tested in practical settings, substantiating the findings further.
Roberto G. Bellazzi - One of the best experts on this subject based on the ideXlab platform.
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Building a Normative Decision Support System for Clinical and Operational Risk Management in Hemodialysis
IEEE Transactions on Information Technology in Biomedicine, 2008Co-Authors: Chiara Cornalba, Roberto G. BellazziAbstract:This paper describes the design and implementation of a decision support system for risk management in hemodialysis (HD) departments. The proposed system exploits a domain ontology to formalize the problem as a Bayesian network. It also relies on a software tool, able to automatically collect HD data, to learn the network conditional probabilities. By merging prior knowledge and the available data, the system allows to estimate risk profiles both for patients and HD departments. The risk management process is completed by an Influence Diagram that enables scenario analysis to choose the optimal decisions that mitigate a patient's risk. The methods and design of the decision support tool are described in detail, and the derived decision model is presented. Examples and case studies are also shown. The tool is one of the few examples of normative system explicitly conceived to manage operational and clinical risks in health care environments.
Le Sun - One of the best experts on this subject based on the ideXlab platform.
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an Influence Diagram based cloud service selection approach in dynamic cloud marketplaces
Cluster Computing, 2019Co-Authors: Le SunAbstract:Cloud service selection is one of the key issues for service users. An important concern for this issue is to make an optimal policy that adapt to a dynamic cloud marketplace that different types of changes may happen during a service consumption process, for example, the change of the cloud service providers and the provisioned cloud services in this marketplace, the change of the requirements of service users on service performance, e.g. Quality of services (QoSs) and service functions, and the change of the termination time points of a consumed service. These changes require a dynamic modelling method for cloud service selection, while existing service selection approaches rarely consider such dynamicity. Therefore, we propose a novel Cloud service selection framework based on Markov decision processes (MDPs), which can help Cloud service users to select a set of services meeting the QoS requirements and the economic constraints of service users. The MDP, as one of the primary decision theoretic planning tools, is capable of formalizing the uncertainties in the dynamic marketplace, and making service selection policies to achieve the best trade-off between costs and benefits. We use the causal-mapping approach to construct the structure of Influence Diagrams, and a Gauss kernel estimation method to estimate the marginal distributions of QoSs. Our experiments are based on simulated scenarios and real datasets. The experimental results show that the proposed framework is capable of accurately capturing the features of QoS values, predicting the QoS performance, and efficiently adapting to the changes in a long-term service consumption to facilitate policy determinations in a dynamic marketplace.
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time critical interactive dynamic Influence Diagram
International Journal of Approximate Reasoning, 2015Co-Authors: Yinghui Pan, Yifeng Zeng, Yanping Xiang, Le Sun, Xuefeng ChenAbstract:Multiagent time-critical dynamic decision making is a challenging task in many real-world applications where a trade-off between solution quality and computational tractability is required. In this paper, we present a formal representation for modelling time-critical multiagent dynamic decision problems based on interactive dynamic Influence Diagrams (I-DIDs). The new representation called time-critical I-DIDs (TC-IDIDs) represents space-temporal abstraction by providing time-index to nodes and the model is defined in terms of the condensed and deployed forms. The condensed form is a static model of TC-IDIDs and can be expanded into its dynamic version. To facilitate the conversion between the two forms, we exploit the notion of object-orientation design to develop flexible and reusable TC-IDIDs. The difficulty on expanding TC-IDIDs is to select a proper time sequence to index nodes in the condensed form so that the expanded TC-IDIDs can be solved efficiently without compromising the quality of the policy. For this purpose, we propose two methods to build the condensed form of TC-IDIDs. We evaluate the solution quality and time complexity in three well-studied problems and provide results in support. Propose a new decision model for solving time-critical multiagent decision making problems.Present an effective method to construct the model.Propose two methods to solve the model.Experiment the methods in three problem domains.
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an Influence Diagram approach for multiagent time critical dynamic decision modeling
Pacific Rim International Conference on Artificial Intelligence, 2010Co-Authors: Le Sun, Yifeng Zeng, Yanping XiangAbstract:Recent interests in multiagent dynamic decision modeling in partially observable multiagent environments have led to the development of several representation and inference methods. However, these methods have limited application under time-critical conditions where a trade-off between model quality and computational tractability is essential. We present a formal representation for modeling time-critical multiagent dynamic decision problems through interactive dynamic Influence Diagrams. The proposed model, called interactive time-critical dynamic Influence Diagrams, has the ability to represent space-temporal abstraction in multiagent dynamic decision models. More importantly, we take the notion of object-orientation design which facilitates the self-expansion and self-compression in the model implementation.