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In this paper, we address the problem of Navigation Among Movable Obstacles (NAMO): a practical extension to navigation for humanoids and other dexterous mobile robots. The robot is permitted to reconfigure the environment by moving obstacles and clearing free space for a path. This paper presents a resolution complete planner for a subclass of NAMO problems. Our planner takes advantage of the navigational structure through state-space decomposition and heuristic search. The planning complexity is reduced to the difficulty of the specific navigation task, rather than the dimensionality of the multi-object domain. We demonstrate real-time results for spaces that contain large numbers of movable obstacles. We also present a practical framework for single-agent search that can be used in algorithmic reasoning about this domain.

The ISO56002 international standard for managing innovation systems was published in 2019. In this paper, we review the rationale, the key features, and the evidence base for this new standard. The primary objective of the standard is to promote the professionalisation of the field by providing a framework for management and organisational practice. The standard was developed by a wide range of stakeholders, including consultants and professional associations, and therefore features most of the elements we would expect from such a high-level, generic approach: strategy, organisation, leadership, planning, support, process, performance evaluation, and improvement. We examine the empirical base for each of these components in this paper. We also identify some critical shortcomings, such as the implicit adoption of a linear model, lack of specific tools to support practice, or any significant variation in application by sector or context. Finally, we recommend how the standard could be improved and implemented in practice.

This work develops and implements a multi-agent time-based path-planning method using A*. The purpose of this work is to create methods in which multi-agent systems can coordinate actions and complete them at the same time. We utilized A* with constraints defined by a dynamic model of each agent. The model for each agent is updated during each time step and the resulting control is determined. This results in a translational path that each of the agents is physically capable of completing in synchrony. The resulting path is given to the agents as a sequence of waypoints. Periodic updates of the path are calculated, utilizing real-world position and velocity information, as the agents complete the task to account for external disturbances. Our methodology is tested in a dynamic simulation environment as well as on real-world lighter-than-air robotic agents.

With the current explosion of data, retrieving and integrating information from various sources is a critical problem. Work in multidatabase systems has begun to address this problem, but it has primarily focused on methods for communicating between databases and requires significant effort for each new database added to the system. This paper describes a more general approach that exploits a semantic model of a problem domain to integrate the information from various information sources. The information sources handled include both databases and knowledge bases, and other information sources (e.g. programs) could potentially be incorporated into the system. This paper describes how both the domain and the information sources are modeled, shows how a query at the domain level is mapped into a set of queries to individual information sources, and presents algorithms for automatically improving the efficiency of queries using knowledge about both the domain and the information sources. This work is implemented in a system called SIMS and has been tested in a transportation planning domain using nine Oracle databases and a Loom knowledge base.

Ecological impact of sanitary landfill is discussed in this chapter. Landfill design, leachate control, and landfill gas (LFG) management are developed and their applications are illustrated. Landfill pollution control, collection, and treatment of LFG, and utilization of LFG energy are discussed in detail. Cost data and practical examples for planning, LFG management, and operation are presented.

We explore how to represent, plan and learn robot pouring. This is a case study of a complex task that has many variations and involves manipulating non-rigid materials such as liquids and granular substances. Variations of pouring we consider are the type of pouring (such as pouring into a glass or spreading a sauce on an object), material, container shapes, initial poses of containers and target amounts. The robot learns to select appropriate behaviors from a library of skills, such as tipping, shaking and tapping, to pour a range of materials from a variety of containers. The robot also learns to select behavioral parameters. Planning methods are used to adapt skills for some variations such as initial poses of containers. We show using simulation and experiments on a PR2 robot that our pouring behavior model is able to plan and learn to handle a wide variety of pouring tasks. This case study is a step towards enabling humanoid robots to perform tasks of daily living.

Pressing challenges in urban adaptation planning to extreme events include: (1) involving vulnerable populations in the impacted area; and (2) employing a multi-level stakeholder collaborative process to build consensus for action. These processes become even more important as adaptive urban planning is recognized as an effective governance model for adaptation to climate change. In a case study of a low to moderate income community vulnerable to present and increased coastal storm surge flooding, the Supported Community Planning Process was employed because (a) most residents of East Boston affiliate primarily with their own local neighborhoods and (b) the residents need targeted expertise to help them understand some of the scientific and technical aspects of adaptation planning. Collaboration was necessary among three sets of critical stakeholders interested in adaptation strategies in East Boston — the local residents and small businesses, the City of Boston, and the agencies that provide infrastructure services — because some adaptation actions will collectively protect assets of all. The overall process occurred successfully because of positive, knowledgeable, and direct exchange of values and goals. The research illustrates how marginalized populations can be effectively engaged in urban adaptation planning, and how that process can be combined in multi-level stakeholder collaborative planning so that plans might be developed that meet multiple shared and individual goals in a cost-effective manner.

The ability to describe business processes as executable models has always been one of the fundamental premises of workflow management. Yet, the tacit nature of human knowledge is often an obstacle to eliciting accurate process models. On the other hand, the result of process modeling is a static plan of action, which is difficult to adapt to changing procedures or to different business goals. In this article, we attempt to address these problems by approaching workflow management with a combination of learning and planning techniques. Assuming that processes cannot be fully described at build-time, we make use of learning techniques, namely Inductive Logic Programming (ILP), in order to discover workflow activities and to describe them as planning operators. These operators will be subsequently fed to a partial-order planner in order to find the process model as a planning solution. The continuous interplay between learning, planning and execution aims at arriving at a feasible plan by successive refinement of the operators. The approach is illustrated in two simple scenarios. Following a discussion of related work, the paper concludes by presenting the main challenges that remain to be solved.

The study combines the research domains of strategic management and corporate innovation by examining the impact of strategic management practices on entrepreneurial orientation (EO). Recognising the importance of internal business processes that enable firm entrepreneurial behaviour, it is hypothesised that higher levels of EO are positively associated with the strategic management practices of (1) locus of planning, (2) scanning intensity, (3) planning flexibility, (4) planning horizon, and (5) strategy and financial control attributes. Empirical testing takes place in an under-researched emerging market context on a sample of 219 financial and business services firms. The results provide support for the positive impact that the different strategic management practices have on EO. A practical consideration is for managers to leverage the strategic management practices so that the firm's position on the conservative-entrepreneurial continuum is increased by its propensity to be innovative, proactive, and be willing to take risks when confronted by uncertainty.

There have been a large number of attempts to develop a theory or theories of Environmental Impact Assessment in order to justify its use in environmental decision-making. A review of academic articles demonstrates that these theories are largely drawn from planning theories. Planning theories are themselves a development of sociological theories of decision-making and from one particular strand of sociological theory. In this review of the theories of EIA it is argued that an understanding of wider sociological theory is necessary to fully understand both planning and EIA.

This statement aims at giving an overview of the research on impact assessment in Sweden. It takes a point of departure at the introduction of impact assessment in 1991 describing the Swedish research until today. Since the introduction of EIA in the Swedish legislation in the 1990s, a large number of PhD dissertations have been dealing with various aspects of impact assessment. An estimation based on the literature search is that about 20 PhD theses, in which the core of the research is related to EIA or SEA, have been produced since 1990. The research follows four main themes: (i) EIA and SEA as a tool for integration of environmental and sustainability aspects in planning and development, (ii) EIA and SEA as a tool for integration of ecological aspects in planning, (iii) research on the basic concepts of EIA and SEA, and (iv) the relationship with other assessment tools for sustainable development.

There is a growing interest in the potential of strategic environmental assessment (SEA) to mainstream ecosystem services (ES) concerns in decision-making. Experiences in this field have begun to emerge, showing the need for comprehensive guidance. This paper addresses this need by proposing a conceptual approach to integrate ES effectively in SEA. The approach is structured in the following four stages, each comprising specific tasks: establish the ES context; determine and assess priority ES; identify alternatives and assess impacts on ES; follow up on ES. The first stage includes the identification and mapping of ES and beneficiaries for the region affected by the strategic action and the identification of links with other strategic actions. The second stage aims at generating detailed information on a limited set of priority ES, which are considered relevant for shaping and informing the development of the strategic action. This requires determining the priority ES, reviewing existing regulations concerning these services and assessing their baseline conditions and trends. In the third stage, possible alternatives to enhance ES, or at least to minimise negative effects on them, are identified and their impacts assessed. Finally, during the fourth stage, the effects on ES are monitored and managed and the overall quality of the SEA process is tested. The paper concludes by discussing how the stages and their tasks require feedback and interactions and how they can contribute to achieve a better inclusion of concerns about ES (and their beneficiaries) into strategic decisions.

The use of environmental impact assessment (EIA) to address the environmental and social impacts of mining is common. At Ok Tedi mine in Papua New Guinea, and Century mine in northern Australia, EIA failed to protect the biophysical and social environment of the mines. A detailed historical analysis was conducted to determine the chronology of the development of these projects, all internal and external decision making, and the reasons for this failure. This analysis showed that EIA was poorly timed and poorly integrated with the early phases of the mine developments. Environmental assessment expenditure and activity were concentrated in the development phase of the project and could not address impacts resulting from the earlier exploration and evaluation phases. Approval processes, such as permits and licences, that did occur in the early phases of the life of the mines, had very limited components of environmental appraisal or control. The primacy of development schedules, and the lack of communication between actors in the development of these large-scale projects, prohibited effective assessment and produced EIAs that were in many ways unrelated to the real environmental information needs of the project. In such large-scale projects it is essential to link EIA (and other approval/appraisal hoops) to environmental issues and decisions which occur throughout the continuum of mine development.

This paper reports on the development of a computer model that will predict both overall project and activity duration, based on a number of pre-determined project characteristics. Fifty-six programmes of work were obtained. The data from the programmes of work of fifty of these buildings, encompassing a total of 11 different project types, were analysed, and used to develop the proposed model. Multiple linear regression analysis of the data showed that the duration and time lags of between 20 (for a single storey building) and 39 (for a seven-storey building) standardised activity groups, can be predicted using combinations of the twenty one most influential project variables.

The regression equations produced were tested on all of the activity groups for six new projects to determine their accuracy. The absolute percentage error in predicting overall duration varied between 0.38% and 6.68%. The mean absolute error in predicting the duration of activity groups varied between 1.38% and 22%. The accuracy in predicting overall duration was comparable with limited information available from previous studies, but the high level of detail in the programme generated means that the model is more flexible and capable of a broader range of applications than previous models.

The revolutionary innovations in amusement parks, starting with the ‘Bakken’, north of Copenhagen, in 1583 and given modern form as themed entertainment attractions by Disney at Anaheim, California in 1955 have spread outwards through successive imitation and adaptation by the amusement industry. The chapter reviews some of the historical aspects of amusement parks, presents some key data and then goes on to discuss economic and development issues, including park planning and design. Concepts of creativity and issues of failure are examined in order to define the boundaries of what may be currently considered good practice to minimise the downside risks that can result in financial, if not project, collapse. Numerous examples are given so as to establish trends.

The reliability of power distribution network is important. For high reliability, it is necessary for some nodes to have backup connections to other feeders in the network. The substation operator wants to expand the network such that some nodes have k redundant connection lines (i.e., k redundancy) in case the current feeder line does not work. The corporation is given this task to design the expansion planning to construct new connection lines. The substation operator will choose the minimum charged k redundant connection lines based on both of the existing network and the expansion network, which is designed by the corporation. The existing network has the cost for the redundant connection due to the operational expense. The corporation proposes the design with its own price, which may include the operational expense and the construction expense. Thus, for the corporation, how to assign the low price on the connection lines while maximizing the revenue becomes a Stackelberg minimum weight k-star game for the power distribution network expansion.

A heuristic algorithm is proposed to solve this Stackelberg minimum weight k-star game for the power distribution network expansion, using three heuristic rules for price setting in a scenario by scenario fashion. The experimental results show that the proposed algorithm always outperforms the greedy algorithm which is natural to k-star game in terms of corporation revenue. Compared to the greedy algorithm, the proposed algorithm improves up to 60.7% in the corporation revenue in the chosen minimum weight k-star, which is the minimum charged k connection lines. The average improvement is 7.5%. This effectively handles k redundancy in the power distribution network expansion while maximizing the corporation revenue.

Reliability assessments of AI programs must consider not only possible program bugs which. remain in the program due to insufficient testing and debugging, but also faults due to intrinsic characteristics of AI programs that cannot be removed even after the program is fully debugged. This paper develops an analytical tool for assessing the reliability of AI programs. Possible intrinsic faults of AI programs are identified, and modifications to existing software reliability models for conventional programs are suggested. An example illustrating the effect of intrinsic faults of AI heuristics on the program reliability in a real-time situation is given. It is shown that under certain conditions the cost-based A* planning algorithm is less reliable than the node-based A* planning algorithm.

If we want to find the shortest plan, then usually, we try plans of length 1, 2, …, until we find the first length for which such a plan exists. When the planning problem is difficult and the shortest plan is of a reasonable length, this linear search can take a long time; to speed up the process, it has been proposed to use binary search instead. Binary search for the value of a certain parameter x is optimal when for each tested value x, we need the same amount of computation time; in planning, the computation time increases with the size of the plan and, as a result, binary search is no longer optimal. We describe an optimal way of combining planning algorithms into a search for the shortest plan – optimal in the sense of worst-case complexity. We also describe an algorithm which is asymptotically optimal in the sense of average complexity.

Business process (BP) models are usually defined manually by business analysts through imperative languages considering activity properties, constraints imposed on the relations between the activities as well as different performance objectives. Furthermore, allocating resources is an additional challenge since scheduling may significantly impact BP performance. Therefore, the manual specification of BP models can be very complex and time-consuming, potentially leading to non-optimized models or even errors. To overcome these problems, this work proposes the automatic generation of imperative optimized BP models from declarative specifications. The static part of these declarative specifications (i.e. control-flow and resource constraints) is expected to be useful on a long-term basis. This static part is complemented with information that is less stable and which is potentially unknown until starting the BP execution, i.e. estimates related to (1) number of process instances which are being executed within a particular timeframe, (2) activity durations, and (3) resource availabilities. Unlike conventional proposals, an imperative BP model optimizing a set of instances is created and deployed on a short-term basis. To provide for run-time flexibility the proposed approach additionally allows decisions to be deferred to run-time by using complex late-planning activities, and the imperative BP model to be dynamically adapted during run-time using replanning. To validate the proposed approach, different performance measures for a set of test models of varying complexity are analyzed. The results indicate that, despite the NP-hard complexity of the problems, a satisfactory number of suitable solutions can be produced.

Many processes are characterized by high variability, making traditional process modeling languages cumbersome or even impossible to be used for their description. This is especially true in cooperative environments relying heavily on human knowledge. Declarative languages, like Declare, alleviate this issue by not describing what to do step-by-step but by defining a set of constraints between actions that must not be violated during the process execution. Furthermore, in modern cooperative business, time is of utmost importance. Therefore, declarative process models should be able to take this dimension into consideration. Timed Declare has already previously been introduced to monitor temporal constraints at runtime, but it has until now only been possible to provide an alert when a constraint has already been violated without the possibility of foreseeing and avoiding such violations. In this paper, we introduce an extended version of Timed Declare with a formal timed semantics for the entire language. The semantics degenerates to the untimed semantics in the expected way. We also introduce a translation to timed automata, which allows us to detect inconsistencies in models prior to execution and to early detect that a certain task is time sensitive. This means that either the task cannot be executed after a deadline (or before a latency), or that constraints are violated unless it is executed before (or after) a certain time. This makes it possible to use declarative process models to provide a priori guidance instead of just a posteriori detecting that an execution is invalid. We also outline how a Declare model with time can be used in resource planning and how Declare has been integrated into CPN Tools.