Current domain-independent, classical planners require symbolic models of the problem domain and instance as input, resulting in a knowledge acquisition bottleneck. Meanwhile, although deep learning has achieved significant success in many fields, the knowledge is encoded in a subsymbolic representation which is incompatible with symbolic systems such as planners. We propose Latplan, an unsupervised architecture combining deep learning and classical planning. Given only an unlabeled set of image pairs showing a subset of transitions allowed in the environment (training inputs), Latplan learns a complete propositional PDDL action model of the environment. Later, when a pair of images representing the initial and the goal states (planning inputs) is given, Latplan finds a plan to the goal state in a symbolic latent space and returns a visualized plan execution. We evaluate Latplan using image-based versions of 6 planning domains: 8-puzzle, 15-Puzzle, Blocksworld, Sokoban and Two variations of LightsOut.

We formulate and solve the H-SARA Problem, a Vehicle Routing and Appointment Scheduling Problem motivated by home services management. We assume that travel times, service durations, and customer cancellations are stochastic. We use a two-stage process that first generates teams and routes using a VRP Solver with optional extensions and then uses an MC Scheduler that determines expected arrival times by teams at customers. We further introduce two different models of cancellation and their associated impacts on routing and scheduling. Finally, we introduce the Route Fracture Metaheuristic that iteratively improves an H-SARA solution by replacing the worst-performing teams. We present insights into the problem and a series of numerical experiments that illustrate properties of the optimal routing, scheduling, and the impact of the Route Fracture Metaheuristic for both models of cancellation.

E-commerce has long represented both challenges and opportunities for e-tailers and omnichannel retailers. Now, a spotlight has been shown on both aspects of e-commerce by the acceleration of online shopping during the pandemic. Consumer expectations are rising along with e-commerce volumes, and success is being defined from the consumer's perspective. In the process, e-fulfilment and returns management have become complex undertakings that increasingly require the use of advanced automation. This is one of the foremost trends in logistics today -- and, as logistics operations become more reliant on automation for satisfactory results, the combination of humans and machines that is driving efficiency can help improve health and safety as well.

Engaged couples are forced to postpone their weddings due to social distancing measures. Some couples know the value of a dollar. Or in Sian Taylor's case, she and her fiancé Andy know the value of a pound. The young couple got engaged in the U.K. back in May and have been planning their nuptials, which Taylor has been documenting on TikTok. Throughout the planning process, Taylor realized various wedding services and supplies can quickly rack up a large price tag.

Transportation System Planning Fundamentals is a comprehensive video course where you can learn industry specific knowledge about system planning such as that found in the transportation industry. I have handcrafted this course to allow students to acquire core fundamental knowledge on how to develop the key knowledge needed to do effective strategic decision making throughout the transportation planning process. I will teach you the basic concepts of the transportation planning framework as well as different bits and pieces on how to create an effective transportation plan. I will also go over the management and operations of the transportation system so that you will have a solid understanding as to how the transportation system functions. If you are a professional who are interested to work as planner for the transportation industry, you will find this course of great help to get the fundamental knowledge you need to enhance your professional career.

When studying robots collaborating with humans, much of the focus has been on robot policies that coordinate fluently with human teammates in collaborative tasks. However, less emphasis has been placed on the effect of the environment on coordination behaviors. To thoroughly explore environments that result in diverse behaviors, we propose a framework for procedural generation of environments that are (1) stylistically similar to human-authored environments, (2) guaranteed to be solvable by the human-robot team, and (3) diverse with respect to coordination measures. We analyze the procedurally generated environments in the Overcooked benchmark domain via simulation and an online user study. Results show that the environments result in qualitatively different emerging behaviors and statistically significant differences in collaborative fluency metrics, even when the robot runs the same planning algorithm.

Even if path planning can be solved using standard techniques from dynamic programming and control, the problem can also be approached using probabilistic inference. The algorithms that emerge using the latter framework bear some appealing characteristics that qualify the probabilistic approach as a powerful alternative to the more traditional control formulations. The idea of using estimation on stochastic models to solve control problems is not new and the inference approach considered here falls under the rubric of Active Inference (AI) and Control as Inference (CAI). In this work, we look at the specific recursions that arise from various cost functions that, although they may appear similar in scope, bear noticeable differences, at least when applied to typical path planning problems. We start by posing the path planning problem on a probabilistic factor graph, and show how the various algorithms translate into specific message composition rules. We then show how this unified approach, presented both in probability space and in log space, provides a very general framework that includes the Sum-product, the Max-product, Dynamic programming and mixed Reward/Entropy criteria-based algorithms. The framework also expands algorithmic design options for smoother or sharper policy distributions, including generalized Sum/Max-product algorithm, a Smooth Dynamic programming algorithm and modified versions of the Reward/Entropy recursions. We provide a comprehensive table of recursions and a comparison through simulations, first on a synthetic small grid with a single goal with obstacles, and then on a grid extrapolated from a real-world scene with multiple goals and a semantic map.

Path planning, the problem of efficiently discovering high-reward trajectories, often requires optimizing a high-dimensional and multimodal reward function. Popular approaches like CEM and CMA-ES greedily focus on promising regions of the search space and may get trapped in local maxima. DOO and VOOT balance exploration and exploitation, but use space partitioning strategies independent of the reward function to be optimized. Recently, LaMCTS empirically learns to partition the search space in a reward-sensitive manner for black-box optimization. In this paper, we develop a novel formal regret analysis for when and why such an adaptive region partitioning scheme works. We also propose a new path planning method PlaLaM which improves the function value estimation within each sub-region, and uses a latent representation of the search space. Empirically, PlaLaM outperforms existing path planning methods in 2D navigation tasks, especially in the presence of difficult-to-escape local optima, and shows benefits when plugged into model-based RL with planning components such as PETS. These gains transfer to highly multimodal real-world tasks, where we outperform strong baselines in compiler phase ordering by up to 245% and in molecular design by up to 0.4 on properties on a 0-1 scale.

Havelock Ellis said it is not the attainment of the goal that matters, it is the things met with by the way. He was speaking of philosophy. In business AI is all about goal attainment. The things met along the way are decisions. Decisions constitute a focus of the recent survey by Signal AI of 1,000 C-suite executives in an attempt to estimate the impact of AI on the U.S. economy.

Although relocation of human populations is nothing new, global environmental changes such as climate change, sea level rise, and land use change are increasing the likelihood of relocation for potentially millions of people, especially in coastal regions. Globally, sea level rise alone could place 340 million people on land projected to be below annual flood levels by 2050 ([ 1 ][1]). The need for relocation will increase because of such risks, the lack of funding for protection and accommodation strategies, and/or the reality that sea walls and other measures will eventually be ineffective. Thus, current approaches to planned relocation such as buyouts for individual households are likely to be “woefully inadequate” in the future ([ 2 ][2]). We discuss how science, governance, and their interactions need to evolve to make planned relocation a strategic option that leaves people, communities, and the environment better off. The starting point is to acknowledge that relocation involves a physical transition away from locations exposed to global change hazards, as well as the need for transformation of institutions, social networks, cultural associations, economic relationships, and other aspects of a community's way of life. Given that relocation is a life-altering change, organizations such as the United Nations (UN) High Commission on Refugees mandate that it needs to be planned and implemented with meaningful engagement of affected parties and carried out to improve (or at least maintain) their quality of life. To ensure responsiveness to changing conditions and preferences, relocation should be part of a flexible, nested, and interconnected set of adaptation strategies that also include coping (reactive, short-term risk-reduction measures) and incremental adjustments (measures to increase resistance and/or resilience) ([ 3 ][3]). How to combine these different measures into a strategic portfolio of policies and actions places demands on science and governance to support open-ended adaptive planning processes that manage trade-offs across interests, uncertainties in knowledge, and institutional ambiguity created by overlapping jurisdictions, authorities, and expertise. Planned relocation is a complex social dilemma that involves many structural, perceptual, economic, and interpersonal dynamics that discourage collective action. It will involve resolving fraught questions such as what decision processes are used, who relocates (and when), how are they compensated, where will they move, what assistance is provided (and to whom) in receiving communities, how abandoned wastes and environmental legacies are remediated, and how agreements are monitored and enforced. There is no single best approach to move a community—stakeholders with conflicting objectives will see it differently even when they share basic world views. The interaction of social and environmental triggers and lack of a preferred pathway make planned retreat similar to other global change dilemmas. But the potential scope, existential character of needed transformations, and complexity of governance challenges make it especially demanding. Despite the immensity of the challenge, it is vital now to constructively engage science and governance to plan physical transitions and socioeconomic transformations that reduce risk and make people, communities, and the environment better off. Here, we offer several ideas for improving governance partnerships in developing strategies for planned relocation. ### Eliminate perverse incentives and establish inclusive governance Existing institutions and processes of governance will be stretched to address the challenges of planning and implementing relocation in a way that meets basic humanitarian principles and good practices. This is because current mixes of policies, institutions, and relationships are responsible for producing the prevailing distribution of privilege and vulnerability in society. Although climate change plays a role, it amplifies present challenges that are an amalgam of past governance, entrenched inequities, and norms. The sheer potential scale of relocation globally is beyond anything our modern global society has experienced. For example, the megacity Jakarta is actively considering relocation because of growing climate hazards, aquifer subsidence, and the density of a highly vulnerable low-income population. These challenges are not limited to the developing world, as evidenced by the mounting annual damages and recovery costs of climate extremes on populations in the United States. Improving governance will require addressing structural inequalities and many perverse incentives and behavioral dynamics that continue to drive people to settle in areas exposed to hazards. Innovations are needed to address organizational silos, poor planning and risk communication, psychological attachments to place, and dependence on continued occupation for tax revenues. These challenges can be exacerbated with well-intentioned coping strategies (e.g., the “levee effect” that reduces accurate perception of risk). In the United States, for example, federal programs including subsidization of beach nourishment, the National Flood Insurance Program, and the federalization of natural disaster recovery encourage settlement of risky areas. Planned relocation toolkits ([ 4 ][4]) are beginning to emerge that orient the challenge within domestic legal frameworks and international organizations (e.g., the UN Office for Disaster Risk Reduction) and the experiences garnered from existing national efforts (e.g., Fiji's efforts to move 46 villages). Making and implementing decisions in which communities voluntarily relocate will require inclusive, deliberative processes that emphasize transparency, engagement, trust building, accountability, and an interactive approach for engaging with science. Policy or legislative frameworks are critical to defining long-term targets and providing credible commitments to maintain the continuity of objectives across institutions and political mandates ([ 5 ][5]). Strategies will need to accommodate changing circumstances (new scientific evidence, technological change, new preferences) and the management of implementation tactics based on expert advice, monitoring and reporting, and accountability. In most countries, new institutions and funding are required to improve access to expert advice, coordination, and consultation. Governance frameworks for relocation will need to include periodic communication about future risks, engagement with private sector and civil society, and oversight mechanisms to monitor and enforce the implementation of agreed plans. ### Diverse perspectives in problem framing Defining the problem and its context is the central challenge posed by planned relocation. Framing a problem establishes what is prioritized (and what is treated as unimportant), what the objectives are, and what questions will be asked and answered. Framing is often contested, and to avoid marginalizing communities, it needs to incorporate diverse perspectives, start from the specific local context of ongoing systemic challenges, enhance stakeholders' agency, and bring together diverse sources of knowledge ([ 6 ][6], [ 7 ][7]). It is particularly challenging to carefully analyze the diverse stakeholders and the types of knowledge that are pivotal to understanding and framing planned relocation (e.g., capturing perspectives from the relocating, receiving, and remaining populations). Problem framing could consider the need for expertise, tactical engagement, and sustained advocacy to catalyze plans into transformative actions ([ 6 ][6], [ 8 ][8]). In addition, emerging innovations in computational social science and “coproduction” of research (in which stakeholder communities are involved in different aspects of the scientific process) offer opportunities for formalizing stakeholder analysis. Analyses could improve stakeholder identification, categorization, and relationship (power) mapping. ### Account for power dynamics Decades of research in planning, public administration, sustainability science, and science and technology studies have examined how to improve the relevance and effectiveness of science to inform planning and policy for a wide range of social, environmental, and sustainability challenges. Several prominent strands of this work focus on coproduction as being more than a means to produce science, providing a mechanism to generate public goods, services, and institutions ([ 7 ][7]). Accordingly, the design of coproduction processes is not just about how the interactions of policy-makers, stakeholders, and scientists affect the usability of science. It is also about the process of social change—how epistemologies, social and cultural norms, institutions and policies, and power relationships among communities and stakeholders interact to determine who is involved in the process, which types of knowledge are seen as legitimate, what is produced, and what outcomes result. For challenges as fraught as planned relocation, this more expansive approach provides a foundation for codeveloping knowledge and action. It requires engaging multiple perspectives on values and knowledge where the actors involved in coproduction of planned retreat must work together to explore normative and political differences inherent in their different visions of the future ([ 6 ][6]). A critique of coproduction processes is that they can depoliticize discourse by using scientific arguments to evoke universalized ideas of what is “best.” They can be structured as if all participants have an equal role when in fact governments, large nongovernmental organizations, and economic interests have disproportionate power and greater opportunities for participation ([ 7 ][7]). This is not just a process issue but can also affect the outcomes of coproduction—for example, favoring the use of narrow cost-benefit framings that conclude that protective measures such as beach nourishment or construction of sea walls are economically justified only for high-value assets. Empirically informed awareness of the diverse roles and dimensions of power in coproduction and social change offers an avenue for rebalancing problematic relationships that lead to inequality or exclusion, or at least avoiding their unintended consequences ([ 7 ][7]). Modest steps such as providing funding to enable underserved communities to participate in coproduction, or formalizing the participation of Indigenous advisory councils, can also help level the playing field ([ 9 ][9]). ### Diversify knowledge sources and types To support planned relocation, science needs to deliver not just technical solutions but also knowledge of how to relocate and transform communities, including the willingness and capacities of different groups and institutions to support fundamental change over time ([ 6 ][6]). Providing this knowledge will require a transdisciplinary approach to research that broadens the array of scientific disciplines and other sources of knowledge engaged. Government bodies and stakeholders (e.g., real estate interests, businesses, community-based organizations) will need to be integrated into research not just as “users” but as knowledge holders and experts in community needs, preferences, norms, and evolving capacity to implement solutions. When relocation involves Indigenous communities, rather than integrating traditional knowledge into Western science, scientists involved in coproduction arrangements should foster mutual respect on the multiple ways of knowing, by engaging in tribal avenues, such as regional newsletters and talking circles at tribal meetings ([ 9 ][9], [ 10 ][10]). Informing social and economic transformation will require research into the capacities and values of different populations and institutions. This requires understanding issues such as what will motivate people to make changes, the capacity of individuals and institutions to act on their preferences, and how current conditions and path dependencies affect the viability of future options ([ 6 ][6]). It will be necessary to “think critically about outcomes as well as processes, about institutional and process designs, [and] about power and performance” ([ 11 ][11]). ### Sample from a range of plausible futures to evaluate decision options Science can better inform action if it stops trying to predict inherently unpredictable phenomena. Currently, many decision-makers frame their questions to scientists as “what will happen,” and scientists respond with “projections” (possibilities based on assumptions about future radiative forcing), which are often used as predictions. This framing, in addition to putting science in the dangerous position of speculating, is not necessarily as helpful to decision-makers as “what if” questions about the consequences of options under many plausible futures. Science can be more useful by changing the objective of collaboration from “predict then act” to the exploration of hypothetical questions about what short-term actions would be consistent with long-term objectives and perform well under a diverse range of plausible futures ([ 12 ][12]). As a specific example, the State of Louisiana has been confronting sea level rise, land subsidence, accelerating losses of coastal lands, and increasing risks from storm surge. The state has initiated an innovative and collaborative planning process that budgets $50 billion in a portfolio of projects to be adaptively implemented over the next 50 years ([ 13 ][13]). Unlike traditional cost-benefit–driven risk planning efforts based on a specific expected future (“what will happen”), the Louisiana master plan has engaged broad stakeholder participation to map what project investments hold immediate benefits while providing flexibility to confront a broad range of plausible future scenarios that could reshape their investment priorities as well as future stakeholder needs (“what if” planning). This approach recognizes that many types of uncertainty will impede judgment and decision-making ([ 12 ][12]). The natural stressors that can trigger the need for evacuation are uncertain because they are emergent, compounding, and cascading outcomes of complex human–environment interactions. But the implications of changes in future values and behaviors are also uncertain and arguably just as important for evaluating decision options. Even in well-documented historical instances of relocation, it is difficult to understand how outcomes emerged from the actions taken—let alone anticipate with any certainty how desired outcomes arise from future actions ([ 14 ][14]). One important opportunity is to more widely apply decision-making under deep uncertainty (DMDU) methods ([ 12 ][12]). These exploratory approaches draw on local-scale stakeholders' knowledge of the key factors and dynamics (human and natural) and provide a promising mechanism for informing planned relocation. Models and scenarios serve as focal points to build shared understanding about the potential implications of the different values and options preferred by stakeholders. ### Social learning to build local capacity Relocation is a complex process that will benefit from expanding the range of intermediaries and services available to facilitate production and application of knowledge. Those involved will need to know not only what scientifically robust sources of information are available for the hazards they face, but also how this information should be used to assess vulnerability, revise flood maps or zoning, evaluate financial risks to reset insurance rates and bond ratings, redesign infrastructure systems, update capital improvement and other plans, or establish thresholds and monitoring systems to trigger the next phase of agreed measures. Much attention has focused on providing climate scenarios and data, but to meet the needs of relocation, the range of services must be expanded. Needed services include not only identifying good practices in engineering, financial risk, and other technical analyses but also supporting transformation, capacity building, and establishment of standards for different types of deliberative and analytic processes. Research, case studies, and pilot projects are testing approaches to meet these challenges, and a useful next step is to organize evaluation and social learning to establish good practices and technical guidance. One option is to incorporate evaluation into assessments such as the Intergovernmental Panel on Climate Change and the US National Climate Assessment to establish a knowledge foundation for climate services. This would create standards for services delivered through international organizations, the private sector, academia, and public agencies (to ensure availability of services for underserved, low-income communities) ([ 15 ][15]). Another is an open-source wiki for climate solutions that would enable a more diverse range of knowledge holders to interact and curate guidance on good practices on an ongoing basis, emphasizing sources of credible information. Another opportunity is to expand the use of intermediaries—individuals and institutions that facilitate interactions between stakeholders and experts ([ 8 ][8]). Many intermediary skillsets are necessary for the different stages of deliberative planning, financing, tactical implementation, and ex-post monitoring of relocation actions. Given the potential for contested needs and values, it is important that intermediaries be aware of how they can unintentionally affect power relationships or outcomes—for example, by using types of knowledge, analysis metrics, or visualizations that favor the perspectives of one group or another. A “critical pragmatic approach” highlights the importance of this awareness and of designing and critically evaluating deliberative processes where conflicts between parties are not reduced to simple consensus-driven debates ([ 11 ][11]). A variety of measures are needed to increase the number and efficacy of intermediaries, including professional certification; greater recognition, including in promotion and tenure processes; and increased funding. ### Harness emerging innovations in community science and data analytics Innovations in community science, sensing, and data analytics hold great promise in providing insights for planned relocation if privacy, equity, and other concerns such as maladaptive applications of generic algorithmic or sensing tools are addressed ([ 15 ][15]). Combining these innovations with monitoring investments in socioeconomic data offers the potential to better capture the interdependent evolution of human and natural systems that shape the experiences and prospects of populations facing relocation. For example, high-resolution models of flooding magnitude and extent might be available for an area, but data are missing on how inequities in agency and justice interact with exposure to hazards to shape the prospects of using planned relocation to improve people's lives. These innovations will increase the utility of standard modes of multidisciplinary scientific research that combine hazard predictions, engineering, financial, and other analyses to inform technical solutions that contribute to physical transitions. Additional methodological advances that have not yet been fully exploited include improved projections of hazards at various spatial scales; research on coastal habitat loss and nature-based solutions; new data sources, indicator-based assessments, and demographic modeling to identify vulnerable populations; and practice standards for using global change risk analytics in engineering and other professions. 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