Making and Keeping Probabilistic Commitments for Trustworthy Multiagent Coordination

In a large number of real world domains, such as the control of autonomous vehicles, team sports, medical diagnosis and treatment, and many others, multiple autonomous agents need to take actions based on local observations, and are interdependent in the sense that they rely on each other to accomplish tasks. Thus, achieving desired outcomes in these domains requires interagent coordination. The form of coordination this thesis focuses on is commitments, where an agent, referred to as the commitment provider, specifies guarantees about its behavior to another, referred to as the commitment recipient, so that the recipient can plan and execute accordingly without taking into account the details of the provider's behavior. This thesis grounds the concept of commitments into decision-theoretic settings where the provider's guarantees might have to be probabilistic when its actions have stochastic outcomes and it expects to reduce its uncertainty about the environment during execution. More concretely, this thesis presents a set of contributions that address three core issues for commitment-based coordination: probabilistic commitment adherence, interpretation, and formulation. The first contribution is a principled semantics for the provider to exercise maximal autonomy that responds to evolving knowledge about the environment without violating its probabilistic commitment, along with a family of algorithms for the provider to construct policies that provably respect the semantics and make explicit tradeoffs between computation cost and plan quality. The second contribution consists of theoretical analyses and empirical studies that improve our understanding of the recipient's interpretation of the partial information specified in a probabilistic commitment; the thesis shows that it is inherently easier for the recipient to robustly model a probabilistic commitment where the provider promises to enable preconditions that the recipient requires than where the provider instead promises to avoid changing already-enabled preconditions. The third contribution focuses on the problem of formulating probabilistic commitments for the fully cooperative provider and recipient; the thesis proves structural properties of the agents' values as functions of the parameters of the commitment specification that can be exploited to achieve orders of magnitude less computation for 1) formulating optimal commitments in a centralized manner, and 2) formulating (approximately) optimal queries that induce (approximately) optimal commitments for the decentralized setting in which information relevant to optimization is distributed among the agents.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/162948/1/qizhg_1.pd

Returns on public capital investment - procurement, whole life cost and value in English schools and hospitals from 1997 - 2012

The UK government has for many decades assumed the role of provider for a range of public services (and the assets that underpin them) considered essential to the functioning of society. Education and healthcare in England have remained almost entirely publicly funded under the administration and management of central government departments and local authorities. The need to maintain and invest in the public service assets (PSAs) that support delivery remains, regardless of whether their ownership is public or private. The business cases for investment involve social cost benefit analysis assessed against the budgetary constraints of fiscal affordability. This thesis attempts to identify the information used, and ideally required, to make decisions to invest in building schools and hospitals. The role of procurement method is considered alongside the forms of capital work (refurbishment / new build) in recent capital programmes for schools and hospitals. Theoretical frameworks for analysis of the efficacy of capital investment are drawn from the whole life cost (WLC) literature and discourses on decision making under uncertainty, contract theory and transaction cost economics. New methodological contributions on the valuation of whole life cost returns, including those from improved outcomes in the form of educational attainment in schools, are presented in later analysis chapters. Key findings include: 1) the estimated whole life cost ratio of 1 (construction) to 0.5 (operation) to 5 (staffing) for schools over a 60 year life discounted at 3.5% and, 2) a lack of association in improved educational attainment following capital investment. Further, findings suggest that given the durable nature of PSAs, along with the long time periods over which benefits accrue, there is considerable difficulty in appraising the returns to (and value of) capital investment in PSAs. Recommendations focus on the need for better co-ordination of government data on capital programmes and projects, on-going costs of operation and the outcomes of PSA users to better inform investment appraisal and programme design

The effects of the on-line maintenance work management strategy on nuclear plant performance

Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Nuclear Engineering, 1997.Includes bibliographical references (p. 99-101).by Frederick Mitchell Nielsen.M.Eng

Making Communities More Flood Resilient: The Role of Cost Benefit Analysis and Other Decision-support Tools in Disaster Risk Reduction

Given the series of large-scale flood disasters that have occurred in recent years, there is a growing recognition among community leaders, businesses, insurers, governments and international donors of the need to invest in risk reduction measures before such events happen. Due to the costs of risk reduction measures, these actions need to be justified and as a result there is an increasing need to utilize decision-support tools, which can help to make the case for action to reduce disaster risks and build flood resilience when faced with limited resources. Across stakeholders, the specific objectives from the use of decision-support tools include (i) demonstrating the efficiency of the action ex-ante (before the flood); (ii) aiding in the selection of a particular intervention in enhancing community flood resilience from a suite of possible options; (iii) helping communities make the right choice when faced with limited investments; (iv) demonstrating the benefits of donor funding of community flood resilience projects; and (v) monitoring the successes and weaknesses of past interventions to generate lessons learned for future work. Typically, discussion on decision-support for disaster risk reduction (DRR) in floods (as well as for other hazards) has focused on cost-benefit analysis (CBA), however there are a number of other tools available to support decision-making. These include cost-effectiveness analysis (CEA), multi-criteria analysis (MCA) and robust-decision-making approaches (RDMA), which have been applied to similar problems, and can also be used to aid decision-making regarding flooding. This white paper provides an overview of the opportunities and challenges of applying these different tools, and guides the reader to select among them. Selection depends on the desired objective, circumstances, data available, timeframe to perform analyses, level of detail, and other considerations. We first focus on the CBA decision-tool, as this has been the mainstay of research and implementation. We then go beyond CBA to consider the other techniques for prioritising DRR investments. While our analysis is specific to flood DRR actions, the conclusion are also applicable to other hazards. The key findings arising from this white paper with relevance to research, policy and implementation of flood DRR decision-support tools, are: (1) Following a comprehensive review of the quantitative CBA flood DRR evidence, we find that flood DRR investments largely pay off, with an average of five dollars saved for every dollar spent through avoided and reduced losses; (2) Using CBA for flood risk reduction assessment should properly account for low-frequency, high-impact flood events, and also tackle key challenges such as intangible impacts; (3) Decision-making can be improved by using various decision support tools tailored to the desired outcomes and contexts. This white paper is the foundation upon which the Zurich flood resilience alliance work on integration of a decision toolbox will proceed "on the ground," with established community-based risk assessment tools, in particular Vulnerability Capacity Assessments (VCA) or Participatory Capacity and Vulnerability Assessments (PCVA). Based on these findings we propose a way forward over the next several years on informing risk-based decision making as part of the alliance program

Modeling Probabilistic Commitments for Maintenance Is Inherently Harder than for Achievement

Most research on probabilistic commitments focuses on commitments to achieve enabling preconditions for other agents. Our work reveals that probabilistic commitments to instead maintain preconditions for others are surprisingly harder to use well than their achievement counterparts, despite strong semantic similarities. We isolate the key difference as being not in how the commitment provider is constrained, but rather in how the commitment recipient can locally use the commitment specification to approximately model the provider's effects on the preconditions of interest. Our theoretic analyses show that we can more tightly bound the potential suboptimality due to approximate modeling for achievement than for maintenance commitments. We empirically evaluate alternative approximate modeling strategies, confirming that probabilistic maintenance commitments are qualitatively more challenging for the recipient to model well, and indicating the need for more detailed specifications that can sacrifice some of the agents' autonomy

CAMP-BDI: an approach for multiagent systems robustness through capability-aware agents maintaining plans

Rational agent behaviour is frequently achieved through the use of plans, particularly within the widely used BDI (Belief-Desire-Intention) model for intelligent agents. As a consequence, preventing or handling failure of planned activity is a vital component in building robust multiagent systems; this is especially true in realistic environments, where unpredictable exogenous change during plan execution may threaten intended activities. Although reactive approaches can be employed to respond to activity failure through replanning or plan-repair, failure may have debilitative effects that act to stymie recovery and, potentially, hinder subsequent activity. A further factor is that BDI agents typically employ deterministic world and plan models, as probabilistic planning methods are typical intractable in realistically complex environments. However, deterministic operator preconditions may fail to represent world states which increase the risk of activity failure. The primary contribution of this thesis is the algorithmic design of the CAMP-BDI (Capability Aware, Maintaining Plans) approach; a modification of the BDI reasoning cycle which provides agents with beliefs and introspective reasoning to anticipate increased risk of failure and pro-actively modify intended plans in response. We define a capability meta-knowledge model, providing information to identify and address threats to activity success using precondition modelling and quantitative quality estimation. This also facilitates semantic-independent communication of capability information for general advertisement and of dependency information - we define use of the latter, within a structured messaging approach, to extend local agent algorithms towards decentralized, distributed robustness. Finally, we define a policy based approach for dynamic modification of maintenance behaviour, allowing response to observations made during runtime and with potential to improve re-usability of agents in alternate environments. An implementation of CAMP-BDI is compared against an equivalent reactive system through experimentation in multiple perturbation configurations, using a logistics domain. Our empirical evaluation indicates CAMP-BDI has significant benefit if activity failure carries a strong risk of debilitative consequence

Modeling and Measuring Resilience: Applications in Supplier Selection and Critical Infrastructure

Nowadays, infrastructure systems such as transportation, telecommunications, water supply, and electrical grids, are considerably facing the exposure of disruptive events such as natural disasters, manmade accidents, malevolent attacks, and common failures due to their size, complexity, and interconnectedness nature. For example fragile design of supply chain infrastructure might collapses because the consequences of a failure can propagate easily through the layers of supply chains, especially for large interconnected networks. Previously, owners and operators of infrastructure systems focused to design cost-efficient, competitive and sustainable ones; however the need for design of resilient infrastructure systems is inevitable. Infrastructure systems must be designed in such a way so that they are resistant enough to withstand and recover quickly from disruptions. The consequences of disruptive events on infrastructures ranging from energy systems (e.g., electrical power network, natural gas pipeline) to transportation systems (e.g., food supply chain, public transportation) cannot only impacted on individuals, but also on communities, governments and economics. The goal of this dissertation is to (i) identify the resilience capacities of infrastructure systems; in particular inland waterway ports, and supply chain systems, (ii) quantify and analyze the resilience value of critical infrastructure systems (CIs), (iii) improve the resilience of CIs by simulating different disruptive scenarios, and (iv) recommend managerial implications to help owners and operators of CIs for timely response, preparedness, and quick recovery against disruptive events. This research first identifies the resilience capacity of CIs, in particular, inland waterway, supply chain and electrical power plant. The resilience capacity of CIs is modeled in terms of their absorptive capacity, adaptive capacity and restorative capacity. A new resilience metric is developed to quantify the resilience of CIs. The metric captures the causal relationship among the characteristics of CIs and characteristics of disruptive events including intensity and detection of disruption likelihood of disruptive events. The proposed resilience metric is generic, meaning that can be applied across variety of CIs. The proposed metric measures the system resilience as the sum of degree of achieving successful mitigation and contingency strategies. The resilience metric accounts for subjectivity aspect of disruptive events (e.g., late disruption detection, very intense disruption, etc.). Additionally, the proposed resilience metric is capable of modeling multiple disruptive events occurring simultaneously. This research study further explores how to model the resilience of CIs using graphical probabilistic approach, known as Bayesian Networks (BN). BN model is developed to not only quantify the resilience of CIs but also to predict the behavior of CIs against different disruptive scenarios using special case of inference analysis called forward propagation analysis (FPA), and improvement scenarios on resilience of CIs are examined through backward propagation analysis (BPA), a unique features of BN that cannot be implemented by any other methods such as classical regression analysis, optimization, etc. Of interest in this work are inland waterway ports, suppliers and electrical power plant. Examples of CIs are inland waterway ports, which are critical elements of global supply chain as well as civil infrastructure. They facilitate a cost-effective flow of roughly $150 billion worth of freights annually across different industries and locations. Stoppage of inland waterway ports can poses huge disruption costs to the nation’s economic. Hence, a series of questions arise in the context of resilience of inland waterway ports. How the resilience of inland waterway ports can be modeled and quantified? How to simulate impact of potential disruptive events on the resilience of inland waterway ports? What are the factors contributing to the resilience capacity of inland waterway ports? How the resilience of inland waterway can be improved

Food system sustainability metrics: Policies, quantification, and the role of complexity sciences

The rise of global attention toward sustainability and sustainable development (SD) has provided increased incentives for research development and investment in these areas. Food systems are at the center of human needs and global population growth sustainability concerns. These drives and the need to provide quantified support for related investment projects led to the proliferation of sustainability metrics and frameworks. While questions about sustainability definition and measurement still abound, SD policy design and control increasingly need adequate quantified support instruments. This paper aims to address this need, contributing to a more consistent and integrated application of food system sustainability metrics and quantified management of the implemented solutions. After presenting the relationships between sustainability, resilience, and robustness and summarizing food system sustainability quantification developments so far, we expose complexity sciences’ potential contributions toward SD quantified evaluation, addressing prediction, intangibles, and uncertainty issues. Finding a paramount need to make sense and bring existing sustainability metrics in context for operational use, we conclude that the articulated application of multiple and independent modeling approaches at the micro, meso, and macro levels can better help the development of food SD policies and implemented solution quantified management, with due regard to confidence levels of the results obtained.info:eu-repo/semantics/publishedVersio