Learning Integrated Relational and Continuous Action Models for Continuous Domains.

Long-living autonomous agents must be able to learn to perform competently in novel environments. One important aspect of competence is the ability to plan, which entails the ability to learn models of the agent's own actions and their effects on the environment. This thesis describes an approach to learn action models of environments with continuous-valued spatial states and realistic physics consisting of multiple interacting rigid objects. In such environments, we hypothesize that objects exhibit multiple qualitatively distinct behaviors based on their relationships to each other and how they interact. We call these qualitatively distinct behaviors modes. Our approach models individual modes with linear functions. We extend the standard propositional function representation with learned knowledge about the roles of objects in determining the outcomes of functions. Roles are learned as first-order relations using the FOIL algorithm. This allows the functions modeling individual modes to be "instantiated" with different sets of objects, similar to relational rules such as STRIPS operators. We also use FOIL to learn preconditions for each mode consisting of clauses that test spatial relationships between objects. These relational preconditions naturally capture the interaction dynamics of spatial domains and allow faster learning and generalization of the model. The combination of continuous linear functions, relational roles, and relational mode preconditions effectively capture both continuous and relational regularities prominent in spatial domains. This results in faster and more general action modeling in these domains. We evaluate the algorithm on two domains, one involving pushing stacks of boxes against frictional resistance, and one in which a ball interacts with obstacles in a physics simulator. We show that our algorithm learns more accurate models than locally weighted regression in the physics simulator domain. We also show that relational mode preconditions learned with FOIL are more accurate than continuous classifiers learned with support vector machines and k-nearest-neighbor.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/102383/1/jzxu_1.pd

Observation and negotiation at the cultural shoreline Vietnam, rasquachismo and an architectural practice

This research results from my practices of photography and architecture in the rapidly changing cultural environment of Vietnam, a place where a local culture of localised systems of individual enterprise is being quickly displaced by a system of large multinational entities operating at global scale. I research in two ways: as a photographer observing and reflecting on the social and urban environment and as an architect negotiating responses to our design tasks and practice environment. The practices producing this research have highlighted three central issues: a culture supporting local systems of individual enterprises, an opportunistic approach to seeing and making, and my expanded role as a design professional

Representing Qualitative Action Models for Learning in Complex Virtual Worlds

This thesis addresses the problem of representing and learning qualitative models of behaviour in complex virtual worlds. It presents a novel representation, ‘Q-Systems’, that integrates two existing representation frameworks: qualitative process models and action description languages. QSystems combines the expressive power of both frameworks to allow actions and world dynamics to be modelled in a common way using a representation based on non-deterministic and probabilistic finite state machines. The representation supports learning and planning by using a modular approach that partitions world behaviour into ‘systems’ of objects with specific contexts and a related behaviour. Q-Systems was developed and tested using an agent in a rich simulated world that was created as part of the thesis. The simulation uses a rigid body physics engine to produce complex realistic interactions between objects. An action system and a qualitative vision system were also developed to allow the agent to observe and act in the simulated world. The thesis includes a proposed two stage learning process comprising an initial stage in which ‘histories’ (contextually and temporally restricted sequences of observations) are extracted from interactions with the simulation, and a second stage in which the histories are generalised to create a knowledge base of system models. An algorithm for generating histories is presented and a number of heuristics are implemented and compared. A system for learning generalised models is presented and it is used to assess the suitability of Q-Systems with respect to learning in complex environments. Planning with Q-Systems is demonstrated in an agent which reasons with generalised models to work out how to achieve goals in the simulated world. A simple planning algorithm is described and a variety of issues are explored. Planning with a single system is shown to be relatively straightforward due to the modular nature of Q-Systems. This thesis demonstrates that Q-Systems successfully integrate two different representation frameworks and that they can be used in learning and planning in complex environments. The initial results are promising, but further investigation is required to fully understand the advantages and disadvantages of the Q-System approach compared with existing learning systems. This would involve the development of benchmark problems (currently there are none for this particular domain)

Comparison of EMP and HERO programs

Because of the unique features of electromagnetic pulse (EMP) and Hazardous Electromagnetic Effects on Ordnance (HERO), much research and money has gone into protecting weapon systems and ordnance against it. The EMP and HERO phenomena do have a variety of differences and require differences of hardening technique to protect against it. However, they both involve radiation effects and can prematurely initiate ordnance via the electroexplosive device (EED). Protection of weapon systems and ordnance against electronic damage and upset plus EED initiation takes on more of an art form rather than science once basic principles are applied. Nevertheless by relating these two programs via the initiating temperature of the EED. they can be accurately compared with each other. Because of this observation, the two programs can be effectively combined to work jointly on ordnance hardening and protection including all forms of radiation type hazards, present and future.http://archive.org/details/comparisonofemph00bogaLieutenant. United States NavyApproved for public release; distribution is unlimited

Placing the Academy: Essays on Landscape, Work, and Identity

Twenty-one writers answer the call for literature that addresses who we are by understanding where we are--where, for each of them, being in some way part of academia. In personal essays, they imaginatively delineate and engage the diverse, occasionally unexpected play of place in shaping them, writers and teachers in varied environments, with unique experiences and distinctive world views, and reconfiguring for them conjunctions of identity and setting, here, there, everywhere, and in between.https://digitalcommons.usu.edu/usupress_pubs/1019/thumbnail.jp

Connected mathematics : builiding concrete relationships with mathematical knowledge

Thesis (Ph. D.)--Massachusetts Institute of Technology, Program in Media Arts & Sciences, 1993.Includes bibliographical references (leaves 201-209).by Uriel Jospeh Wilensky.Ph.D

Teacher roles during amusement park visits – insights from observations, interviews and questionnaires

Amusement parks offer rich possibilities for physics learning, through observations and experiments that illustrate important physical principles and often involve the whole body. Amusement parks are also among the most popular school excursions, but very often the learning possibilities are underused. In this work we have studied different teacher roles and discuss how universities, parks or event managers can encourage and support teachers and schools in their efforts to make amusement park visits true learning experiences for their students

HM 23: New Interpretations in Naval History

Selected Papers from the Seventeenth McMullen Naval History Symposium Held at the United States Naval Academy, 15–16 September 2011.https://digital-commons.usnwc.edu/usnwc-historical-monographs/1022/thumbnail.jp

State Antifragility: An Agent-Based Modeling Approach to Understanding State Behavior

This dissertation takes an interdisciplinary approach to understanding what makes states antifragile and why this matters by constructing a parsimonious, first of its kind agent-based model. The model focuses on the key elements of state antifragility that reside along a spectrum of fragility and transverse bidirectionally from fragile to resilient to antifragile given a certain set of environmental conditions. First coined by Nicholas Nassim Taleb and applied to economics, antifragility is a nascent concept. In 2015, Nassim Taleb and Gregory Treverton’s article in Foreign Affairs outlined five characteristics of state antifragility. This project aims to advance the study of anti-fragility in the context of the nation-state beyond these initial contributions by (1) development of three propensity variables associated with antifragility, (2) a new agent-based model to investigate antifragility, and (3) applying the findings of the model and the propensity score theorizing to two case studies. This research posits three propensity variables for a state to become fragile, resilient or antifragile. These variables include learning, power conversion, and agility. Cumulatively, these variables comprise a state’s capacity for dealing with various stressors in the international environment. The agent-based model in this dissertation captures the behavior of a single state when confronted with a stress in a variety of scenarios, forming an essential building block for future work (hinted at in the case studies) involving the interaction between states. The case studies show how the propensity variables, and the model results provide the basis for a distinctive and relatively novel evaluation of the historical record involving the history of the United States in and with Iraq, and the evolving great power rivalry between the United States and China, emphasizing the value of taking antifragility seriously in the context of International Studies