Integrating Recognition and Decision Making to Close the Interaction Loop for Autonomous Systems

Intelligent systems are becoming increasingly ubiquitous in daily life. Mobile devices are providing machine-generated support to users, robots are coming out of their cages in manufacturing to interact with co-workers, and cars with various degrees of self-driving capabilities operate amongst pedestrians and the driver. However, these interactive intelligent systems\u27 effectiveness depends on their understanding and recognition of human activities and goals, as well as their responses to people in a timely manner. The average person does not follow instructions step-by-step or act in a formulaic manner, but instead varies the order of actions and timing when performing a given task. People explore their surroundings, make mistakes, and may interrupt an activity to handle more urgent matters. The decisions that an autonomous intelligent system makes should account for such noise and variance regardless of the form of interaction, which includes adapting action choices and possibly its own goals.While most people take these aspects of interaction for granted, they are complex and involve many specific tasks that have primarily been studied independently within artificial intelligence. This results in open-loop interactive experiences where the user must perform a fixed input command or the intelligent system performs a hard-coded output response---one of the components of the interaction cannot adapt with respect to the other for longer-term back-and-forth interactions. This dissertation explores how developments in plan recognition, activity recognition, intent recognition, and autonomous planning can work together to develop more adaptive interactive experiences between autonomous intelligent systems and the people around them. In particular, we consider a unifying perspective of recognition algorithms that provides sufficient information to dynamically produce short-term automated planning problems, and we present ways to run these algorithms faster for the real-time needs of interaction. This exploration leads to the introduction of the Planning and Recognition Together Close the Interaction Loop (PReTCIL) framework that serves as a first step towards identifying how we can address the problem of closing the interaction loop, in addition to new questions that need to be considered

Walking the bridge from single- to multi-species approaches in Southern African fisheries management

Fisheries management worldwide is in flux with calls for an EAF (Ecosystem Approach to Fisheries) needing to be balanced with the ongoing requirements to provide timeous and realistic assessment-based advice for management ( often with major economic and social consequences), that is typically based on single-species stock assessment models. This thesis is an attempt to walk the bridge from single- to multi-species approaches to fisheries management by developing a ''traditional" single-species stock assessment model that is used for management purposes, assessing possibilities for extending the model to incorporate multi-species effects and evaluating the potential of a range of multi-species approaches to contribute to the fumishment of practical management advice. The South African abalone Haliotis midae fishery is an example of a commercially valuable resource that is currently experiencing a downturn due to a complicated mix of biological, social, political, economic and environmental factors. Core problems include illegal fishing and recent ecosystem change in the form of a movement of rock lobsters Jasus lalandii into a major part of the range of the abalone. It seems that the lobsters have dramatically reduced sea urchin Parechinus angulosus populations, thereby indirectly negatively impacting juvenile abalone, which rely on the urchins for shelter. A spatial and age-structured production model (ASPM) developed as part of this study has provided the basis for management advice for this resource over recent years by projecting abundance trends under alternative future catch levels. The focus is on the main abalone fishery Zones A-D. The model estimates the reduction in juvenile abalone survival due to the ecosystem change extent and estimates the illegal take using a novel fisheries index - the confiscations per unit of policing effort (CPUPE). As a consequence of the recent explosion of poaching activities, the combined Zones A-D model-predicted 2003 poaching estimate of 933 MT ( corresponding to the assumption that, on average, 36% of all poached abalone are confiscated) is more than seven times the legal 2003 commercial TAC for these Zones. Given the complexity of ecosystem processes, there is a need to critically evaluate the tools used to steer this thinking. The focus here is on both the most widely-employed multi-species/ecosystem approach (ECOPATH with ECOSIM or EwE) as well as a scenario in which there is an urgent need (from management) for scientific evaluations to quantify indirect interactions between marine mammals and fisheries. A critical review of EwE highlights some weaknesses related to, for example, the handling of some life history responses such as compensatory changes in natural mortality rates of marine mammals, overcompensatory stock-recruit relationships, inadequate representation of uncertainty, possible problems in extrapolating from the micro-scale to the macro-scale as well as some (not too far-reaching) mathematical inconsistencies in the underlying equations. Strengths include the structured parameterisation :framework, the inclusion of a well-balanced level of conceptual realism, a novel representation of predator-prey interaction terms and the inclusion of a Bayes-like approach (ECORANGER) to take account of the uncertainty associated with values for model inputs. The potential of EwE to contribute to five important multi-species management quandaries in the marine environments off southern Africa and Antarctica is assessed, leading to the conclusion that EwE has limited predictive capability in these contexts. 3 Aspects of the potential application of other multi-species/ecosystem modelling approaches to advise the management of South African fisheries are discussed. In general, reliable predictive ability from such models is likely to be achieved sooner for top predators because relatively fewer links need to be modelled. Accordingly discussion concentrates on the problems of modelling marine mammal-fisheries interactions. Competition is a primary concern, but existing evidence is inconclusive because of the difficulties of substantiating claims that predation by marine mammals is adversely affecting a fishery or vice versa. Numerous species have been implicated in such conflicts, and long-term studies are essential to evaluate relationships between rates of predation and types and densities of available prey, i.e., functional responses. More realistic modelling studies are needed to address operational or management issues. Such models should reflect uncertainty in data and model structure, describe the influence of model assumptions, focus on systems where there is the greatest chance of success, incorporate a sufficient array of ecological links, and include appropriate spatial and temporal scaling for data collection and modelling exercises. In general, GADGET (Globally Applicable Area-Disaggregated Generic Ecosystem Evaluation Tool) and Minimum Realistic Models (MRM) are seen to show the most promise for use as tools to assess indirect effects between marine mammals and fisheries. The hake-seal-fishery interactions off the west coast of southern Africa are discussed as an example and the initiatives being pursued to further this modelling work are summarized. An important message derived from this study concerns the need to couple multi-species/ecosystem models with a simulation framework to take explicit account of uncertainty and management issues

Sidekick agents for sequential planning problems

Thesis (Ph. D.)--Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2013.Cataloged from PDF version of thesis.Includes bibliographical references (pages 127-131).Effective Al sidekicks must solve the interlinked problems of understanding what their human collaborator's intentions are and planning actions to support them. This thesis explores a range of approximate but tractable approaches to planning for AI sidekicks based on decision-theoretic methods that reason about how the sidekick's actions will effect their beliefs about unobservable states of the world, including their collaborator's intentions. In doing so we extend an existing body of work on decision-theoretic models of assistance to support information gathering and communication actions. We also apply Monte Carlo tree search methods for partially observable domains to the problem and introduce an ensemble-based parallelization strategy. These planning techniques are demonstrated across a range of video game domains.by Owen Macindoe.Ph.D

System of Systems Stakeholder Planning in a Multi-Stakeholder, Multi-Objective, and Uncertain Environment

The United States defense planning process is currently conducted in a partially consolidated manner driven by the Joint Capabilities Integration and Development System (JCIDS) process. Decisions to invest in technology, develop systems, and acquire assets are made by individual services with coordination at the higher joint level. These individual service’s decisions are made in an environment where resource allocation and need are influenced by external stakeholders (e.g. shared system development costs, additional levied requirements, and complementary system development). The future outcome of any given decision is subject to a high degree of uncertainty stemming from both the stakeholder execution of a decision and the environment in which that execution will take place. Uncertainty in execution stems from TRL advancement, development timelines, acquisition timelines, and final deployed performance. Environmental uncertainty factors include future stakeholder resource availability, the future threat environment, cooperative stakeholder decisions, and mirrored adversary decisions. The defense planning problem can be described as an acknowledged System of Systems (SoS) planning problem. Today, methodologies exist that individually address SoS Engineering processes, the evaluation of SoS performance, and SoS system deterministic evolution. However, few approaches holistically address the SoS planning and evolution problem at the level needed to assist individual defense stakeholders in strategic planning. Current approaches do not address the impact of multiple-stakeholder decisions, multiple goals for each stakeholder, the uncertainty of decision outcomes, and the temporal component to strategic decision making. This thesis develops and tests a methodology to address defense stakeholder planning in a multi-stakeholder, multi-objective, and uncertain environment. First, a decision space is populated and captured via sampling a game framework that represents multiple stakeholder decisions as well as decision outcomes over time. A compressed Markov Decision Process (MDP) based meta-model is constructed using state-space consolidation techniques. The meta-model is evaluated using a risk-based policy development algorithm derived from combining traditional Reinforcement Learning (RL) techniques with mean-variance portfolio theory. Policy sensitivity to stakeholder risk-tolerance levels is used to develop state-based risk-tolerance sensitivity profiles and identify Pareto efficient actions. The risk-tolerance sensitivity profiles are used to evaluate both state spaces and decision spaces to provide stakeholders with risk-based insights, or rule sets, to support immediate decision making and risk-based stakeholder playbook development. The capability of the risk-based policy algorithm is tested using both elementary and complex scenarios. It is demonstrated that the algorithm can be used to extract Pareto efficient decisions as a function of risk-tolerance. The state space compression is tested via the comparison of the loss of information between the risk-based policy solutions for uncompressed and compressed state space. The full methodology is then demonstrated using a full-complexity scenario based on the joint development by France, Germany, and Spain of the SoS based Future Combat Air System (FCAS). The full complexity scenario is used to baseline the risk-based methodology against current optimal policy solution techniques. A significant increase in resulting derived insights relative to optimal policy solutions in a high uncertainty scenario is demonstrated.Ph.D

Network Revenue Management under Competition within Strategic Airline Alliances

Airlines often cooperate with partners within strategic alliances to offer their customers itineraries beyond their own networks. However, despite the cooperation, the alliance members often remain competitors on other routes and compete for customers. This thesis takes into account the competition between two alliance partners and models it in a linear program. An algorithm is developed to compute optimal capacity allocations in pure Nash equilibria based on the model. For cases, in which a Nash equilibrium does not exist or cannot be found within a pre-defined time, a heuristic approach is described to compute an approximate Nash equilibrium. Computational studies show the applicability of the approaches in real-sized airline networks. Finally, suggestions for future research are made