Agent-based decision support for failure-prone networked infrastructures
05 Nov 2009-International Journal of Critical Infrastructures (Inderscience Publishers)-Vol. 5, Iss: 4, pp 323-339
TL;DR: This work demonstrates a model-based approach to making rational decisions in situations where various stakeholders need to make decisions that are specific to the failure type and bear little resemblance to decisions faced during normal operation.
Abstract: The operation of existing infrastructures is often inefficient and subject to failures When a failure occurs, various stakeholders need to make decisions that are specific to the failure type and bear little resemblance to decisions faced during normal operation In this work, we demonstrate a model-based approach to making rational decisions in such situations Agent-based models serve as a suitable paradigm for modelling complex sociotechnical systems Given the broad similarities between different networked infrastructure systems, an ontology has been developed as the foundation for a 'model factory' for such systems A specific application of this model factory to a refinery supply chain system is described Further, the use of this simulation model for decision support to manage an abnormal situation in the supply chain is reported
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30 Oct 2009
TL;DR: A generic agent-based modelling framework for socio-technical systems is developed and is shown to offer valuable decision support in case studies of an oil refinery supply chain and an intermodal freight hub.
Abstract: What is a suitable modelling approach for socio-technical systems? The answer to this question is of great importance to decision makers in large scale interconnected network systems. The behaviour of these systems is determined by many actors, situated in a dynamic, multi-actor, multi-objective and multi-level jungle. Models to support such an actor should be able to capture both the physical and social reality of the system, their interactions with one another and the external dynamic environment. Moreover, they must allow users to experiment with changes in both the physical and the social network configuration. To deal with these challenges a generic agent-based modelling framework for socio-technical systems is developed in this thesis. The cornerstone of the framework is a shared language formalised in an ontology, which forms the interface needed to bring different elements of the system (both social and physical) together, to interconnect different models and ensure interoperability. The re-usability of building blocks helps modellers build new models more efficiently. The models developed with the new framework are shown to offer valuable decision support in case studies of an oil refinery supply chain and an intermodal freight hub.
80 citations
TL;DR: The need for policy-makers, infrastructure operators and researchers to consider alternative approaches to formulating risk and enabling solutions to challenging 21st century issues related to interdependent infrastructures is discussed.
Abstract: Developing effective protection, mitigation and recovery measures for critical infrastructures is paramount in the wake of increasing natural and human-initiated hazards, risks and threats. Influencing these measures are interconnections (i.e., interdependencies) among infrastructure systems. Understanding the nature of system interdependencies can play an essential role in minimizing and/or reducing the probabilities and consequences of cascading failures in interdependent systems. This paper discusses the need for policy-makers, infrastructure operators and researchers to consider alternative approaches to formulating risk and enabling solutions to challenging 21st century issues related to interdependent infrastructures. Using the healthcare sector as an example, this paper suggests that identifying the risks associated with maintaining public health goes beyond traditional risk formulation to include the structural complexity brought about by infrastructure interdependencies.
37 citations
Dissertation•
01 Apr 2014
12 citations
Cites background from "Agent-based decision support for fa..."
...(2007) and Walker et al. (2008) offer the description of behaviour exhibited by the system in order to further capture its characteristics....
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...According to Walker et al. (2008), STS as a concept is founded on two main principles....
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01 Jan 2014
TL;DR: In this paper, the authors present the LUCRAM Lund University Centre for Risk Analysis and Management (Lucram), which is based at the University of Lund in Sweden, and discuss risk analysis and management.
Abstract: LUCRAM Lunds universitets centrum för riskanalys och riskhantering Lunds universitet Box 118 221 00 Lund http://www.lucram.lu.se LUCRAM Lund University Centre for Risk Analysis and Management Lund University P.O. Box 118 SE-221 00 Lund Sweden http://www.lucram.lu.se
5 citations
Journal Article•
TL;DR: For instance, Walker et al. as mentioned in this paper provide a conceptual basis for the systematic classification of uncertainty in model-based decision support activities, such as policy analysis, inte-grated assessment, and risk assessment.
Abstract: ‘‘Penetrating so many secrets,we cease to believe in the unknowable.But there it sits nevertheless,calmly licking its chops.’’– H.L. Mencken, Minority Report, 1956Policymaking is about the future. If we were able to predictthe future accurately, preferred policies could be identified(at least in principle) by simply examining the future thatwould follow from the implementation of each possiblepolicy and picking the one that produced the most favorableoutcomes. However, for most systems of interest today(particularly social and economic systems), such predictionis not possible, due to their increasing complexity, theirincreasing interrelationships with other systems, and theincreasing uncertainty of developments external to thesystem that have important effects on the system. Wheneven the best model cannot reliably predict the details of asystem’s behavior, the classical approach of choosing apolicy based on the outcomes from a best estimate model isno longer credible. Such policies are ‘best’ for a future thatmost certainly will not occur, and have implications for thefuture that actually occurs that are typically not examined inthe course of policy design and analysis. Current approachesto policy analysis have serious difficulties in dealing withproblems characterized by complexity or disequilibriumbehavior, systems undergoing significant organizational andstructural change, and systems that can only be influencedrather than controlled. Yet, these characteristics haveincreasingly become staple characteristics of the world inwhich we live. Such systems are fundamentally unpredict-able. Yet, rapid economic, political, and social changes are areality, and public policies must be devised in spite ofprofound uncertainties about the future.Even though the future cannot be predicted, it is possibleto prepare for it. If, in the face of massive uncertainties,public policies are to be useful and credible, new approacheswill be needed for dealing with uncertainty. This specialissue of Integrated Assessment is a first step in filling thisneed. The papers in this issue are drawn from a sessionentitled ‘‘Dealing With Uncertainty in Policy Analysis andPolicymaking’’ that was part of the 5th InternationalConference on Technology, Policy and Innovation, whichwas held in The Hague in June 2001.There are five papers in this collection. They can bedivided into two categories:1. How can uncertainty in policy analysis and policymakingbe characterized (what is it? how can it be placed in ahistorical context? how can we classify different types ofuncertainties?)?2. How can policy analysts and policymakers deal withuncertainties (i.e., how can policies be developed thathave a good chance of succeeding in spite of enormousuncertainties about the future?)?1. CHARACTERIZING UNCERTAINTYThat uncertainties exist in practically all policymakingsituations is generally understood by policymakers andpolicy analysts. But there is little appreciation for the factthat there are many different types of uncertainty, and thereis a lack of understanding about their relative magnitudesand the different tools that are appropriate to use for dealingwith the different types. Even within the community ofpolicy analysts who deal with uncertainty in their work,there is no commonly shared terminology, and no agreementon a typology of uncertainties. The first paper (by Walker,Harrem€ooes, Rotmans, van der Sluijs, van Asselt, Janssen,and von Krauss) aims to provide a conceptual basis for thesystematic classification of uncertainty in model-baseddecision support activities, such as policy analysis, inte-grated assessment, and risk assessment. As van Asselt [1]notes, any typology of uncertainties is context dependent. Infact, according to her, uncertainty type by definition ‘‘refersto the way in which uncertainty manifests itself in aparticular context.’’ The context for the typology ofuncertainty presented in this paper is model-based decisionsupport. The authors first define uncertainty in model-based
5 citations
References
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TL;DR: Names of founding work in the area of Adaptation and modiication, which aims to mimic biological optimization, and some (Non-GA) branches of AI.
Abstract: Name of founding work in the area. Adaptation is key to survival and evolution. Evolution implicitly optimizes organisims. AI wants to mimic biological optimization { Survival of the ttest { Exploration and exploitation { Niche nding { Robust across changing environments (Mammals v. Dinos) { Self-regulation,-repair and-reproduction 2 Artiicial Inteligence Some deenitions { "Making computers do what they do in the movies" { "Making computers do what humans (currently) do best" { "Giving computers common sense; letting them make simple deci-sions" (do as I want, not what I say) { "Anything too new to be pidgeonholed" Adaptation and modiication is root of intelligence Some (Non-GA) branches of AI: { Expert Systems (Rule based deduction)
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TL;DR: This paper describes a mechanism for defining ontologies that are portable over representation systems, basing Ontolingua itself on an ontology of domain-independent, representational idioms.
Abstract: To support the sharing and reuse of formally represented knowledge among AI systems, it is useful to define the common vocabulary in which shared knowledge is represented. A specification of a representational vocabulary for a shared domain of discourse—definitions of classes, relations, functions, and other objects—is called an ontology. This paper describes a mechanism for defining ontologies that are portable over representation systems. Definitions written in a standard format for predicate calculus are translated by a system called Ontolingua into specialized representations, including frame-based systems as well as relational languages. This allows researchers to share and reuse ontologies, while retaining the computational benefits of specialized implementations. We discuss how the translation approach to portability addresses several technical problems. One problem is how to accommodate the stylistic and organizational differences among representations while preserving declarative content. Another is how to translate from a very expressive language into restricted languages, remaining system-independent while preserving the computational efficiency of implemented systems. We describe how these problems are addressed by basing Ontolingua itself on an ontology of domain-independent, representational idioms.
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TL;DR: It will be argued that the development of robust and scalable software systems requires autonomous agents that can complete their objectives while situated in a dynamic and uncertain environment, that can engage in rich, high-level social interactions, and that can operate within flexible organisational structures.
Abstract: Agent-based computing represents an exciting new synthesis both for Artificial Intelligence (AI) and, more generally, Computer Science. It has the potential to significantly improve the theory and the practice of modeling, designing, and implementing computer systems. Yet, to date, there has been little systematic analysis of what makes the agent-based approach such an appealing and powerful computational model. Moreover, even less effort has been devoted to discussing the inherent disadvantages that stem from adopting an agent-oriented view. Here both sets of issues are explored. The standpoint of this analysis is the role of agent-based software in solving complex, real-world problems. In particular, it will be argued that the development of robust and scalable software systems requires autonomous agents that can complete their objectives while situated in a dynamic and uncertain environment, that can engage in rich, high-level social interactions, and that can operate within flexible organisational structures.
1,606 citations