Auswahl der wissenschaftlichen Literatur zum Thema „Learning for planning“

Fieldwork education is a crucial component of social work education. Many social work students regard their placement experiences as the most profound learning experiences of their studies. The students undertake their field placements in a diverse range of organisational contexts, and in so doing perform a myriad of tasks, adopt a variety of roles, implement a range of practices, and engage with numerous people. Needless to say, social work students have a rich set of learning opportunities within such diversity. An important part of the fieldwork process is the development of learning plans; these plans guide and direct the students’ roles, tasks and learning, and are often an important framework by which assessment of competency and learning takes place. However, learning plans presuppose a logical and conceptual clarity, which needs to be learned if they are to be functional and effective documents. This then poses many challenges in relation to how students might develop a learning plan for fieldwork. This paper explores some of the problems, and offers practical guidance, for students and fieldwork educators to develop rational learning plans in diverse and complex contexts.

This paper introduces a framework for Planning while Learning where an agent is given a goal to achieve in anenvironment whose behavior is only partially known to the agent. We discuss the tractability of various plan-design processes. We show that for a large natural class of Planning while Learning systems, a plan can be presented and verified in a reasonable time. However, coming up algorithmically with a plan, even for simple classes of systems is apparently intractable. We emphasize the role of off-line plan-design processes, andshow that, in most natural cases, the verification (projection) part canbe carried out in an efficient algorithmic manner.

TechniCity (Technology and Cities) offered in May, 2013 was the first city planning Massive Open Online Course (MOOC). More than 21,000 students registered for the course, which was composed of video lectures, projects, assignments, peer evaluation, and on-line discussion over a four week period. This MOOC experimented with using field based learning, combined with extensive student engagement. The objective was to extend the type of learning environment typically found in city planning classes and similar to what is being done in several other disciplines. This article describes the first stage of research, describing course structure and providing initial findings on both course and student outcomes. Compared to students enrolling in traditional, for-credit classes, students in this MOOC reported a range of backgrounds, motivations, and expectations. The data collected also provide insights on student course activity including completion. This information obtained from the class can be used to improve future course offerings. This article documents a pedagogical approach that is still very new and lacking a significant base of literature and comparative studies. The article conclude by suggesting a variety of topics for further research.

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Urban Studies and Planning, 2013.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (p. 240-271).
This dissertation examines new professional practices in urban planning that utilize new types of spatial planning support systems (PSS) based on geographic information systems (GIS) software. Through a mixed-methods research design, the dissertation investigates the role of these new technologies in planning workshops, processes, and as metropolitan infrastructures. In particular, PSS are viewed as supporting social learning in spatial planning processes. The study includes cases in Boston, Kansas City, and Austin. The findings indicate high levels of social learning, broadly confirming the collaborative planning theory literature. Participants at planning workshops that incorporated embodied computing interaction designs reported higher levels of two forms of learning drawn from Argyris and Schöns' theory of organizational learning: single and double loop learning. Single loop learning is measured as reported learning. Double loop learning, characterized by deliberation about goals and values, is measured with a novel summative scale. These workshops utilized PSS to contribute indicators to the discussion through the use of paper maps for input and human operators for output. A regression analysis reveals that the PSS contributed to learning by encouraging imagination, engagement, and alignment. Participantsʼ perceived identities as planners, personality characteristics, and frequency of meeting attendance were also related to the learning outcomes. However, less learning was observed at workshops with many detailed maps and limited time for discussion, and exercises lacking PSS feedback. The development of PSS infrastructure is investigated by conducting a qualitative analysis of focus groups of professional planners, and a case where a PSS was planned but not implemented. The dissertation draws on the research literatures on learning, PSS and urban computer models, and planning theory. The research design is influenced by a sociotechnical perspective and design research paradigms from several fields. The dissertation argues social learning is required to achieve many normative goals in planning, such as institutional change and urban sustainability. The relationship between planning processes and outcomes, and implications of information technology trends for PSS and spatial planning are discussed.
by Robert Goodspeed.
Ph.D.

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2008.
Includes bibliographical references (p. 104-108).
Through the combined use of regression techniques, we will learn models of the uncertainty propagation efficiently and accurately to replace computationally intensive Monte- Carlo simulations in informative path planning. This will enable us to decrease the uncertainty of the weather estimates more than current methods by enabling the evaluation of many more candidate paths given the same amount of resources. The learning method and the path planning method will be validated by the numerical experiments using the Lorenz-2003 model [32], an idealized weather model.
by Sooho Park.
S.M.

Optimal sequential decision making is a fundamental problem to many diverse fields. In recent years, Reinforcement Learning (RL) methods have experienced unprecedented success, largely enabled by the use of deep learning models, reaching human-level performance in several domains, such as the Atari video games or the ancient game of Go. In contrast to the RL approach in which the agent learns a policy from environment interaction samples, ignoring the structure of the problem, the planning approach for decision making assumes known models for the agent's goals and domain dynamics, and focuses on determining how the agent should behave to achieve its objectives. Current planners are able to solve problem instances involving huge state spaces by precisely exploiting the problem structure that is defined in the state-action model. In this work we combine the two approaches, leveraging fast and compact policies from learning methods and the capacity to perform lookaheads in combinatorial problems from planning methods. In particular, we focus on a family of planners called width-based planners, that has demonstrated great success in recent years due to its ability to scale independently of the size of the state space. The basic algorithm, Iterated Width (IW), was originally proposed for classical planning problems, where a model for state transitions and goals, represented by sets of atoms, is fully determined. Nevertheless, width-based planners do not require a fully defined model of the environment, and can be used with simulators. For instance, they have been recently applied in pixel domains such as the Atari games. Despite its success, IW is purely exploratory, and does not leverage past reward information. Furthermore, it requires the state to be factored into features that need to be pre-defined for the particular task. Moreover, running the algorithm with a width larger than 1 in practice is usually computationally intractable, prohibiting IW from solving higher width problems. We begin this dissertation by studying the complexity of width-based methods when the state space is defined by multivalued features, as in the RL setting, instead of Boolean atoms. We provide a tight upper bound on the amount of nodes expanded by IW, as well as overall algorithmic complexity results. In order to deal with more challenging problems (i.e., those with a width higher than 1), we present a hierarchical algorithm that plans at two levels of abstraction. A high-level planner uses abstract features that are incrementally discovered from low-level pruning decisions. We illustrate this algorithm in classical planning PDDL domains as well as in pixel-based simulator domains. In classical planning, we show how IW(1) at two levels of abstraction can solve problems of width 2. To leverage past reward information, we extend width-based planning by incorporating an explicit policy in the action selection mechanism. Our method, called π-IW, interleaves width-based planning and policy learning using the state-actions visited by the planner. The policy estimate takes the form of a neural network and is in turn used to guide the planning step, thus reinforcing promising paths. Notably, the representation learned by the neural network can be used as a feature space for the width-based planner without degrading its performance, thus removing the requirement of pre-defined features for the planner. We compare π-IW with previous width-based methods and with AlphaZero, a method that also interleaves planning and learning, in simple environments, and show that π-IW has superior performance. We also show that the π-IW algorithm outperforms previous width-based methods in the pixel setting of Atari games suite. Finally, we show that the proposed hierarchical IW can be seamlessly integrated with our policy learning scheme, resulting in an algorithm that outperforms flat IW-based planners in Atari games with sparse rewards.
La presa seqüencial de decisions òptimes és un problema fonamental en diversos camps. En els últims anys, els mètodes d'aprenentatge per reforç (RL) han experimentat un èxit sense precedents, en gran part gràcies a l'ús de models d'aprenentatge profund, aconseguint un rendiment a nivell humà en diversos dominis, com els videojocs d'Atari o l'antic joc de Go. En contrast amb l'enfocament de RL, on l'agent aprèn una política a partir de mostres d'interacció amb l'entorn, ignorant l'estructura del problema, l'enfocament de planificació assumeix models coneguts per als objectius de l'agent i la dinàmica del domini, i es basa en determinar com ha de comportar-se l'agent per aconseguir els seus objectius. Els planificadors actuals són capaços de resoldre problemes que involucren grans espais d'estats precisament explotant l'estructura del problema, definida en el model estat-acció. En aquest treball combinem els dos enfocaments, aprofitant polítiques ràpides i compactes dels mètodes d'aprenentatge i la capacitat de fer cerques en problemes combinatoris dels mètodes de planificació. En particular, ens enfoquem en una família de planificadors basats en el width (ample), que han tingut molt èxit en els últims anys gràcies a que la seva escalabilitat és independent de la mida de l'espai d'estats. L'algorisme bàsic, Iterated Width (IW), es va proposar originalment per problemes de planificació clàssica, on el model de transicions d'estat i objectius ve completament determinat, representat per conjunts d'àtoms. No obstant, els planificadors basats en width no requereixen un model de l'entorn completament definit i es poden utilitzar amb simuladors. Per exemple, s'han aplicat recentment a dominis gràfics com els jocs d'Atari. Malgrat el seu èxit, IW és un algorisme purament exploratori i no aprofita la informació de recompenses anteriors. A més, requereix que l'estat estigui factoritzat en característiques, que han de predefinirse per a la tasca en concret. A més, executar l'algorisme amb un width superior a 1 sol ser computacionalment intractable a la pràctica, el que impedeix que IW resolgui problemes de width superior. Comencem aquesta tesi estudiant la complexitat dels mètodes basats en width quan l'espai d'estats està definit per característiques multivalor, com en els problemes de RL, en lloc d'àtoms booleans. Proporcionem un límit superior més precís en la quantitat de nodes expandits per IW, així com resultats generals de complexitat algorísmica. Per fer front a problemes més complexos (és a dir, aquells amb un width superior a 1), presentem un algorisme jeràrquic que planifica en dos nivells d'abstracció. El planificador d'alt nivell utilitza característiques abstractes que es van descobrint gradualment a partir de decisions de poda en l'arbre de baix nivell. Il·lustrem aquest algorisme en dominis PDDL de planificació clàssica, així com en dominis de simuladors gràfics. En planificació clàssica, mostrem com IW(1) en dos nivells d'abstracció pot resoldre problemes de width 2. Per aprofitar la informació de recompenses passades, incorporem una política explícita en el mecanisme de selecció d'accions. El nostre mètode, anomenat π-IW, intercala la planificació basada en width i l'aprenentatge de la política usant les accions visitades pel planificador. Representem la política amb una xarxa neuronal que, al seu torn, s'utilitza per guiar la planificació, reforçant així camins prometedors. A més, la representació apresa per la xarxa neuronal es pot utilitzar com a característiques per al planificador sense degradar el seu rendiment, eliminant així el requisit d'usar característiques predefinides. Comparem π-IW amb mètodes anteriors basats en width i amb AlphaZero, un mètode que també intercala planificació i aprenentatge, i mostrem que π-IW té un rendiment superior en entorns simples. També mostrem que l'algorisme π-IW supera altres mètodes basats en width en els jocs d'Atari. Finalment, mostrem que el mètode IW jeràrquic proposat pot integrar-se fàcilment amb el nostre esquema d'aprenentatge de la política, donant com a resultat un algorisme que supera els planificadors no jeràrquics basats en IW en els jocs d'Atari amb recompenses distants.
La toma secuencial de decisiones óptimas es un problema fundamental en diversos campos. En los últimos años, los métodos de aprendizaje por refuerzo (RL) han experimentado un éxito sin precedentes, en gran parte gracias al uso de modelos de aprendizaje profundo, alcanzando un rendimiento a nivel humano en varios dominios, como los videojuegos de Atari o el antiguo juego de Go. En contraste con el enfoque de RL, donde el agente aprende una política a partir de muestras de interacción con el entorno, ignorando la estructura del problema, el enfoque de planificación asume modelos conocidos para los objetivos del agente y la dinámica del dominio, y se basa en determinar cómo debe comportarse el agente para lograr sus objetivos. Los planificadores actuales son capaces de resolver problemas que involucran grandes espacios de estados precisamente explotando la estructura del problema, definida en el modelo estado-acción. En este trabajo combinamos los dos enfoques, aprovechando políticas rápidas y compactas de los métodos de aprendizaje y la capacidad de realizar búsquedas en problemas combinatorios de los métodos de planificación. En particular, nos enfocamos en una familia de planificadores basados en el width (ancho), que han demostrado un gran éxito en los últimos años debido a que su escalabilidad es independiente del tamaño del espacio de estados. El algoritmo básico, Iterated Width (IW), se propuso originalmente para problemas de planificación clásica, donde el modelo de transiciones de estado y objetivos viene completamente determinado, representado por conjuntos de átomos. Sin embargo, los planificadores basados en width no requieren un modelo del entorno completamente definido y se pueden utilizar con simuladores. Por ejemplo, se han aplicado recientemente en dominios gráficos como los juegos de Atari. A pesar de su éxito, IW es un algoritmo puramente exploratorio y no aprovecha la información de recompensas anteriores. Además, requiere que el estado esté factorizado en características, que deben predefinirse para la tarea en concreto. Además, ejecutar el algoritmo con un width superior a 1 suele ser computacionalmente intratable en la práctica, lo que impide que IW resuelva problemas de width superior. Empezamos esta tesis estudiando la complejidad de los métodos basados en width cuando el espacio de estados está definido por características multivalor, como en los problemas de RL, en lugar de átomos booleanos. Proporcionamos un límite superior más preciso en la cantidad de nodos expandidos por IW, así como resultados generales de complejidad algorítmica. Para hacer frente a problemas más complejos (es decir, aquellos con un width superior a 1), presentamos un algoritmo jerárquico que planifica en dos niveles de abstracción. El planificador de alto nivel utiliza características abstractas que se van descubriendo gradualmente a partir de decisiones de poda en el árbol de bajo nivel. Ilustramos este algoritmo en dominios PDDL de planificación clásica, así como en dominios de simuladores gráficos. En planificación clásica, mostramos cómo IW(1) en dos niveles de abstracción puede resolver problemas de width 2. Para aprovechar la información de recompensas pasadas, incorporamos una política explícita en el mecanismo de selección de acciones. Nuestro método, llamado π-IW, intercala la planificación basada en width y el aprendizaje de la política usando las acciones visitadas por el planificador. Representamos la política con una red neuronal que, a su vez, se utiliza para guiar la planificación, reforzando así caminos prometedores. Además, la representación aprendida por la red neuronal se puede utilizar como características para el planificador sin degradar su rendimiento, eliminando así el requisito de usar características predefinidas. Comparamos π-IW con métodos anteriores basados en width y con AlphaZero, un método que también intercala planificación y aprendizaje, y mostramos que π-IW tiene un rendimiento superior en entornos simples. También mostramos que el algoritmo π-IW supera otros métodos basados en width en los juegos de Atari. Finalmente, mostramos que el IW jerárquico propuesto puede integrarse fácilmente con nuestro esquema de aprendizaje de la política, dando como resultado un algoritmo que supera a los planificadores no jerárquicos basados en IW en los juegos de Atari con recompensas distantes.

Policies for people with learning disabilities, as in the case with other groups of service users, have increasingly emphasised the importance of their involvement in the planning of their own services, and at a more general level in the development of their local authority community care plan and commissioning strategies. This thesis seeks to begin to explore some of the difficulties that may arise in attempting to implement such a policy through a case study of practices in one inner London Borough. The study includes a number of important interrelated themes including: the complexities of communication, normalisation, the nature of choice, citizenship and free will, and asks does social policy reform provision or create unrealistic expectations and burdens for social service professionals and service users. It is essentially a study about communication and its impact on choice and social inclusion. Focusing on communication between professionals and service users, their carers and advocates, the field study investigates the Council's strategic planning procedures in order to explore the relationship between service development and the preferences expressed by users. The findings are presented within a legislative framework, with particular interest paid to the government's White Papers 'Modernising Social Services,' 'Valuing People,' and the Best Value initiative. The study combines an historical account of policy development, and investigates social policies that have attempted to bring about change, while also exposing the contradictions within and between them. Because of this there are many challenges attached to this enterprise, and as a consequence the study is inevitably on a small scale and the answers it produces are tentative. Nevertheless it provides an indication of the nature and scale of the difficulties which social services will have to overcome if they are to make a reality of government policy in this area by engaging effectively with the personal experiences and lives of adults with learning disabilities and their carers.

Abstract The aim of the study is to reveal the nature of learning in local land-use planning activity and to examine the possibilities for the development of planning as a form of learning activity. The theoretical approach draws on the pragmatist and dialectical reorientation of systems theory and the related theory of learning organizations. The traditional, positivist systems approach to land-use planning is considered both to depoliticize planning and to make it unreflective. Critical theory as a basis of planning theory is also shown to be inadequate. Communicative planning theories that draw on critical theory are rather theories of emancipation in the context of planning than theories of planning per se. An alternative systems-theoretical view to land-use planning activity is presented, where critical and constructive aspects as well as ethical and pragmatic aspects are interlinked in the dialectical dynamics of planning as organizational and inter-organizational learning activity. Three subsystems within the system of local land-use planning are identified: expertise, politics and economics. The subsystems of land-use planning build upon the basic distinction between legitimate and illegitimate conduct. For each subsystem, the context of its existence is formed by the interaction of all subsystems. By acting, each subsystem inevitably changes its dialectical relationship to this context. Harmful changes are felt within the subsystem as inner contradictions that interfere with its decision-making activity. If the subsystem is unable to face these contradictions but instead resorts to the use of pathological power, they may develop into paralyzing double bind situations. The resolution of a double bind situation requires expansive learning by the subsystem. However, there are also contradictions in land-use planning that the subsystems are unable to resolve by expansive learning. Such inter-systemic contradictions stem from the dialectical relationship between the overriding requirement of legitimacy on one hand and the basic goals of expert knowledge and economic profit on the other. In the study a hypothesis is formulated, according to which these basic - and, in the conditions of modern society, permanent - contradictions in local land-use planning require such inter-organizational learning, which enables the creation of planning solutions that provide means for their task-related harmonization, and, in the longer term, contributes to the emergence of a participative planning culture where the contradictions can be handled legitimately, if not resolved.