853,106 research outputs found
Fully Observable Non-deterministic Planning as Assumption-Based Reactive Synthesis
We contribute to recent efforts in relating two approaches to automatic synthesis, namely, automated planning and discrete reactive synthesis. First, we develop a declarative characterization of the standard “fairness” assumption on environments in non-deterministic planning, and show that strong-cyclic plans are correct solution concepts for fair environments. This complements, and arguably completes, the existing foundational work on non-deterministic planning, which focuses on characterizing (and computing) plans enjoying special “structural” properties, namely loopy but closed policy structures. Second, we provide an encoding suitable for reactive synthesis that avoids the naive exponential state space blowup. To do so, special care has to be taken to specify the fairness assumption on the environment in a succinct manner.Fil: D'ippolito, Nicolás Roque. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Investigación en Ciencias de la Computación. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Investigación en Ciencias de la Computación; ArgentinaFil: Rodriguez, Natalia. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Computación; ArgentinaFil: Sardina, Sebastian. RMIT University; Australi
Facilitating professional engagement with planning research
The context for this project is the limited connectivity between applied planning research and professional planning practice. The planning profession, by its very nature, is continually developing plans, policies and strategies to guide place-based management and development. An assumption guiding the research is that sound evidence is useful if not essential to inform good planning practice. This assumption does not hold for all planning practice - statutory planning and other policy implementation activities are, for example, largely informed by existing policy frameworks. However, in most strategic planning or policy development contexts (including statutory reform), an argument for the relevance of an evidence base can be made. While not all research aims to directly inform practice – such as research of a conceptual or theoretical nature – there is a significant amount of applied urban research produced that has discernible implications for policy and practice.
Unfortunately, much of the research base that could inform and improve professional planning practice is difficult to access. There are also other barriers to knowledge exchange, including limited professional engagement with research outputs; and limited or poorly tailored research outputs for a professional audience. This project aims to provide recommendations on how to better connect Australian urban planning practice to the evidence base within urban planning research outputs. To do so the project explores barriers to, and opportunities for, better connecting professional planning practice with applied planning research
Municipal Property Acquisition Patterns in a Shrinking City: Evidence for the Persistence of an Urban Growth Paradigm in Buffalo, NY
The purpose of this article is to examine municipal property acquisition patterns in shrinking cities. We use data from the City of Buffalo’s municipal property auction records to analyze the spatial distribution of properties offered for sale in its annual tax foreclosure auction. In addition to these data, we examine demolition and building permit records. Our analysis suggests that cities like Buffalo follow strategies based on an urban growth paradigm when responding to abandonment. This paradigm operates under the assumption that growth is a constant and urban development is only limited by fiscal constraints, underdeveloped systems of urban governance, environmental degradation, and resistance by anti-growth coalitions. We recommend that planners in shrinking cities de-emphasize growth based planning and focus on rightsizing strategies. These strategies are based on the assumption that growth is not a constant. Consequently, urban revitalization is concentrated in a smaller urban footprint
Why do masterplans fail?
Planning systems are in general addressed to make spatial projects conform to a plan, by assigning use rights in land through legally binding zoning maps and implementation rules, as it was possible to predict and impose sequences of actions and reactions in the realm of urban development. The cultural ideals of hierarchy and of dirigisme, based on the assumption that the State is the keeper of the collective interest, lie at the root of such ‘conforming' setting of modern planning systems. Neither the reiterated evidence of failure nor the acknowledgment that collective interest is usually the primary victim of planning ineffectiveness have led to conceive true alternatives so far. However, the exception of few countries where plans are non-binding and public authorities can evaluate which specific development projects are deserving new land use rights (the UK is one rare but prominent example), on the one hand, and the increasing experience of EU urban and spatial development programmes implying responsible evaluation mechanisms for co-funding projects, on the other, might let reflect about a possible model of ‘performing' planning syste
Active Markov Information-Theoretic Path Planning for Robotic Environmental Sensing
Recent research in multi-robot exploration and mapping has focused on
sampling environmental fields, which are typically modeled using the Gaussian
process (GP). Existing information-theoretic exploration strategies for
learning GP-based environmental field maps adopt the non-Markovian problem
structure and consequently scale poorly with the length of history of
observations. Hence, it becomes computationally impractical to use these
strategies for in situ, real-time active sampling. To ease this computational
burden, this paper presents a Markov-based approach to efficient
information-theoretic path planning for active sampling of GP-based fields. We
analyze the time complexity of solving the Markov-based path planning problem,
and demonstrate analytically that it scales better than that of deriving the
non-Markovian strategies with increasing length of planning horizon. For a
class of exploration tasks called the transect sampling task, we provide
theoretical guarantees on the active sampling performance of our Markov-based
policy, from which ideal environmental field conditions and sampling task
settings can be established to limit its performance degradation due to
violation of the Markov assumption. Empirical evaluation on real-world
temperature and plankton density field data shows that our Markov-based policy
can generally achieve active sampling performance comparable to that of the
widely-used non-Markovian greedy policies under less favorable realistic field
conditions and task settings while enjoying significant computational gain over
them.Comment: 10th International Conference on Autonomous Agents and Multiagent
Systems (AAMAS 2011), Extended version with proofs, 11 page
Rational planning and politicians' attitudes to spending and reform: replication and extension of a survey experiment
The rational planning cycle of formulating strategic goals and using performance information to assess implementation is assumed to assist decision-making by politicians. Empirical evidence for this assumption is, however, scarce. Our study replicates Nielsen and Baekgaard’s (2015) experiment on the relation between performance information and politicians’ attitudes to spending and reform and extends this experiment by investigating the role of strategic goals. Based on a randomized survey experiment with 1.484 Flemish city councilors and an analysis of 225 municipal strategic plans, we found that information on low and high performance as well as strategic goals directly impact decision-making by politicians
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