5,846 research outputs found

    Optimal Planning with State Constraints

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    In the classical planning model, state variables are assigned values in the initial state and remain unchanged unless explicitly affected by action effects. However, some properties of states are more naturally modelled not as direct effects of actions but instead as derived, in each state, from the primary variables via a set of rules. We refer to those rules as state constraints. The two types of state constraints that will be discussed here are numeric state constraints and logical rules that we will refer to as axioms. When using state constraints we make a distinction between primary variables, whose values are directly affected by action effects, and secondary variables, whose values are determined by state constraints. While primary variables have finite and discrete domains, as in classical planning, there is no such requirement for secondary variables. For example, using numeric state constraints allows us to have secondary variables whose values are real numbers. We show that state constraints are a construct that lets us combine classical planning methods with specialised solvers developed for other types of problems. For example, introducing numeric state constraints enables us to apply planning techniques in domains involving interconnected physical systems, such as power networks. To solve these types of problems optimally, we adapt commonly used methods from optimal classical planning, namely state-space search guided by admissible heuristics. In heuristics based on monotonic relaxation, the idea is that in a relaxed state each variable assumes a set of values instead of just a single value. With state constraints, the challenge becomes to evaluate the conditions, such as goals and action preconditions, that involve secondary variables. We employ consistency checking tools to evaluate whether these conditions are satisfied in the relaxed state. In our work with numerical constraints we use linear programming, while with axioms we use answer set programming and three value semantics. This allows us to build a relaxed planning graph and compute constraint-aware version of heuristics based on monotonic relaxation. We also adapt pattern database heuristics. We notice that an abstract state can be thought of as a state in the monotonic relaxation in which the variables in the pattern hold only one value, while the variables not in the pattern simultaneously hold all the values in their domains. This means that we can apply the same technique for evaluating conditions on secondary variables as we did for the monotonic relaxation and build pattern databases similarly as it is done in classical planning. To make better use of our heuristics, we modify the A* algorithm by combining two techniques that were previously used independently ā€“ partial expansion and preferred operators. Our modified algorithm, which we call PrefPEA, is most beneficial in cases where heuristic is expensive to compute, but accurate, and states have many successors

    Extending classical planning with state constraints: Heuristics and search for optimal planning

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    We present a principled way of extending a classical AI planning formalism with systems of state constraints, which relate - sometimes determine - the values of variables in each state traversed by the plan. This extension occupies an attractive middle ground between expressivity and complexity. It enables modelling a new range of problems, as well as formulating more efficient models of classical planning problems. An example of the former is planning-based control of networked physical systems - power networks, for example - in which a local, discrete control action can have global effects on continuous quantities, such as altering flows across the entire network. At the same time, our extension remains decidable as long as the satisfiability of sets of state constraints is decidable, including in the presence of numeric state variables, and we demonstrate that effective techniques for cost-optimal planning known in the classical setting - in particular, relaxation-based admissible heuristics - can be adapted to the extended formalism. In this paper, we apply our approach to constraints in the form of linear or non-linear equations over numeric state variables, but the approach is independent of the type of state constraints, as long as there exists a procedure that decides their consistency. The planner and the constraint solver interact through a well-defined, narrow interface, in which the solver requires no specialisation to the planning contextThis work was supported by ARC project DP140104219, ā€œRobust AI Planning for Hybrid Systemsā€, and in part by ARO grant W911NF1210471 and ONR grant N000141210430

    Progress in AI Planning Research and Applications

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    Planning has made significant progress since its inception in the 1970s, in terms both of the efficiency and sophistication of its algorithms and representations and its potential for application to real problems. In this paper we sketch the foundations of planning as a sub-field of Artificial Intelligence and the history of its development over the past three decades. Then some of the recent achievements within the field are discussed and provided some experimental data demonstrating the progress that has been made in the application of general planners to realistic and complex problems. The paper concludes by identifying some of the open issues that remain as important challenges for future research in planning

    Core Challenge 2022: Solver and Graph Descriptions

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    This paper collects all descriptions of solvers and ISR instances submitted to CoRe Challenge 2022

    Learning Hierarchical Task Networks Using Semantic Word Embeddings

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    This thesis describes WORD2HTN, which is a novel and semantic approach for learning hierarchical task networks (HTN) and semantic division of goals from input plan traces. The semantic relationships are learned using machine learning to get the vector representations of the components of the plan trace. The semantic relationships are used to learn hierarchical landmarks, which in turn are used to make semantically divided HTNs. These learned HTNs can then be used for subsequent new problems in the domain that have a similar structure with the problems in the input plan traces. This work also improves the learning algorithm to include arithmetic conditions and effects. WORD2HTN was tested on 3 deterministic domains. These are Logistics or Transportation domain, Abstract Graph domain, and the Malmo interface for the Minecraft game. We show that WORD2HTN learns semantically divided HTNs. We also experimentally demonstrate that HTN planners using this have an exponential speedup in information-dense domains over the state of the art classical planner. Finally, we show that the HTNs learned in Minecraft can be used to achieve tasks faster with a cooperative agent controlled by the HTN plannerā€™s output

    The Metric-FF Planning System: Translating "Ignoring Delete Lists" to Numeric State Variables

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    Planning with numeric state variables has been a challenge for many years, and was a part of the 3rd International Planning Competition (IPC-3). Currently one of the most popular and successful algorithmic techniques in STRIPS planning is to guide search by a heuristic function, where the heuristic is based on relaxing the planning task by ignoring the delete lists of the available actions. We present a natural extension of ``ignoring delete lists'' to numeric state variables, preserving the relevant theoretical properties of the STRIPS relaxation under the condition that the numeric task at hand is ``monotonic''. We then identify a subset of the numeric IPC-3 competition language, ``linear tasks'', where monotonicity can be achieved by pre-processing. Based on that, we extend the algorithms used in the heuristic planning system FF to linear tasks. The resulting system Metric-FF is, according to the IPC-3 results which we discuss, one of the two currently most efficient numeric planners

    Spatial data potential for resettlement programmes in local communities vulnerable to debris-flow disasters

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    Resettlement programmes have been implemented by many governments and organisations to relocate people from the hazard areas to other safe places where they are expected to have normal or better lives. However, often the resettled communities face numerous difficulties while going through the relocation process and beyond. It appears that many social and humanitarian problems exists in most of the resettlement programmes (Menoni and Pesaro, 2008). It has been often found that the social, economic and humanitarian problems faced by resettlement communities are linked with the spatial aspects of the resettlement area (Dikmen, 2002; Corsellis and Vitale, 2005; Muggah, 2008). In order to mitigate the severity of those issues in conducting a potential resettlement programme, the information of vulnerable hazard communities must be prepared for the resettlement plan. However, the limitation of the data, i.e. spatial and non-spatial data, of the vulnerable hazard communities plays an important role to delay the post-disaster reduction tasks. An attempt to obtain and develop the dataset potential for post-disaster risk reduction proceed with the resettlement programme requires a comprehensive statement of situations during the disaster occurrence in the hazard community. Therefore, this paper presents a technique identifying the relationships between spatial and nonspatial data essential to the post-disaster risk reduction at the local scale. The obtain information derives from the deep insight interviews of affected people regarding issues associated with spatial aspects in a disaster event. The explored issues regarding the interrelationship between socioeconomic issues and spatial conditions were presented in cognitive maps showing the complexity of those issues in a resettlement programme. As the outcome of the paper, it presents the developed spatail database for resettlement programmes in local communities vulnerable to debris-flow disasters. The explored result of this paper is expected to apply with the resettlement programme in order to prevent the misleading resettlement programmes and also accelerate the post-disaster risk reduction for vulnerable hazard communities effectively

    The use of visual cues for vehicle control and navigation

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    At least three levels of control are required to operate most vehicles: (1) inner-loop control to counteract the momentary effects of disturbances on vehicle position; (2) intermittent maneuvers to avoid obstacles, and (3) outer-loop control to maintain a planned route. Operators monitor dynamic optical relationships in their immediate surroundings to estimate momentary changes in forward, lateral, and vertical position, rates of change in speed and direction of motion, and distance from obstacles. The process of searching the external scene to find landmarks (for navigation) is intermittent and deliberate, while monitoring and responding to subtle changes in the visual scene (for vehicle control) is relatively continuous and 'automatic'. However, since operators may perform both tasks simultaneously, the dynamic optical cues available for a vehicle control task may be determined by the operator's direction of gaze for wayfinding. An attempt to relate the visual processes involved in vehicle control and wayfinding is presented. The frames of reference and information used by different operators (e.g., automobile drivers, airline pilots, and helicopter pilots) are reviewed with particular emphasis on the special problems encountered by helicopter pilots flying nap of the earth (NOE). The goal of this overview is to describe the context within which different vehicle control tasks are performed and to suggest ways in which the use of visual cues for geographical orientation might influence visually guided control activities
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