Search

Optimal facility layout design greatly reduces material handling cost, required floor area and dead space. The usual approaches do not consider material handling aspects and fail to provide an efficient manufacturing environment. This paper proposes a distinct methodology that integrates material handling equipment selection and facility layout design for a heavy manufacturing environment using knowledge-based and optimization technique. The algorithm was coded using C++ language. The applicability of the proposed hybrid methodology is simulated on a Pentium III, 550 MHz computer and applied to two projects involving 6 machines, 30 moves and 12 material handling equipment sets.

The most interesting places can often be the most dangerous ones, and space exploration is no exception. The dynamic surfaces of comets, the turbulent atmospheres of the gas giants, and the hypothesized subsurface ocean of Europa all call for exploration, and are all very risky for on-site robotic explorers. The Casper (continuous activity, scheduling, planning, execution, and replanning) software system provides critical reasoning capabilities to robotic explorers. To show how it works, we first describe Casper's internal workings and then look at the Three Corner Sat and the Autonomous Sciencecraft Constellation missions, highlighting Casper's contributions to each.

Care pathways are excellent tools for the standardization of care delivery and the improvement of clinical efficiency. The high dynamism and unpredictability of the clinical environment require pathways to be adaptable to the deviations that may arise during their execution. In this work we present a methodology for the identification, monitoring, detection and managing of these deviations. Such deviations include evolving patient conditions, arbitrary medical modifications and unpredicted clinical settings. The care pathways are dynamically generated based on a knowledge-driven planning process that personalizes treatments according to up-to-date patient conditions and guarantees adherence to clinical guidelines recommendations. The implementation of the proposed methodology in a domain-independent continuous planning architecture is presented.

Logistics service providers deal with the challenge of estimating future freight transport demands due to the increasing volatility. Short-term planning horizons and planning uncertainties cause reduced capacity utilization and increasing empty mileage. Due to higher flexibility of road transportation, planning uncertainties in freight transport lead to using the road as a preferential mode of transport in most cases. Thus, more ecologically sustainable, but less flexible modes of transport as rail or waterway are omitted. To overcome the aforementioned challenge, a multistep approach is proposed to continuously plan future transport capacity in order to redesign and adjust the intermodal fleet accordingly. The proposed multistep approach combines methods of forecasting, multi-period intermodal routing and decision support for capacity adjustments. Finally, we present first results on a use-case of outbound logistics in automotive industry and give an outlook on our future research.

This chapter discusses the application of intelligent planning techniques to virtual agent environments as a mechanism to control and generate plausible virtual agent behaviour. The authors argue that the real world-like nature of intelligent virtual environments (IVEs) presents issues that cannot be tackled with a classic, off-line planner where planning takes place beforehand and execution is performed later, based on a set of precompiled instructions. What IVEs call for is continuous planning, a generative system that will work in parallel with execution, constantly re-evaluating world knowledge and adjusting plans according to new data. The authors argue further on the importance of incorporating the modelling of the agents’ physical, mental and emotional states as an inherent feature in a continuous planning system targeted towards IVEs, necessary to achieve plausibility in the produced plans and, consequently, in agent behaviour.

This paper describes a continuous planning framework to be used by a planning agent situated within an environment, which reasons about goals with priorities and deadlines, and actions with duration. The framework assumes that goals may be generated continuously, which requires the interleaving of planning and execution. Constraints upon time may mean it is not possible for all goals to be achieved-as a consequence the planning agent must be able to prioritise its goals. A crucial component of this framework is a temporal manager which enables the planner to reason about whether or not there is sufficient time available to achieve all goals, and to calculate deadlines for actions and outstanding subgoals. The main contribution of this paper is an examination of the way in which the partial order planning paradigm could be extended to reason with PDDL2.1 level 3 durative actions.

In this paper we discuss the “safe to act” problem, a problem associated with the safe interleaving of acting and planning. We also discuss previous research that is relevant to this problem. We then propose a specific search strategy for a general hierarchical plan-space planner that pushes portions of the emerging plan to become “execution ready” as quickly as possible. Finally, we discuss a property, critical serialisability, that is sufficient for a domain to possess in order for these portions to be “safely” executed.

The responsibilities of the JFACC (Joint Forces Air Component Commander) are made challenging by a number of factors. JFACC are generally complex, open-ended, and broad in scope. Operating environments are dynamic, unpredictable, and typically hostile. Uncertainty is inherent, with the result that complete and accurate knowledge of the world can never be attained. For these reasons, successful operation requires a mix of long-range planning, responsiveness to unexpected events, and dynamic adaption of activity. Constructed plans must be living objects that evolve and grow over time in response to new tasks, updated information about the world, and result from partial execution through portions of the plan. SRI's project within the JFACC program sought to address these problems by developing a prototype "Continuous Planning and Execution Framework" (CPEF) that provides automated tools for the development, maintenance, and execution of living plans for highly dynamic environments such as JFACC, using a range of selected artificial intelligence (AI) technologies.

At the beginning of the 14th century, Bruges (or Brugge) was one of Europe’s most important cities with a population that equaled those of Paris, London, Bologna and Seville. The source of its economic importance lay in the fact that it was the principal market for northwest European trade at that time.

Program committee members of the ICAPS 2012 Special Track on Continuous Planning

Planning systems need to be endowed with some additional features to cope effectively with execution: e.g., the ability to keep the plan database updated with respect to the actual feedbacks provided by the controlled system, to mention but one. In this paper, we identify a set of noteworthy planning and execution open issues relatively to the timeline-based planning approach. We address those issues presenting a domain independent deliberative system, implemented on top of the APSI-TRF, the A PSI Timeline-based Representation Framework, extended with timeline dispatching and execution-supervision capabilities so as to allow continuous planning and closed-loop re-planning activities. Some ongoing research directions are also briefly introduced.

We have previously demonstrated that, in preparing themselves to aim voluntary impulses of isometric elbow force to unpredictable targets, subjects selected default values for amplitude and direction according the range of targets that they expected. Once a specific target appeared, subjects specified amplitude and direction through parallel processes. Amplitude was specified continuously from an average or central default; direction was specified stochastically from one of the target directions. Using the same timed response paradigm, we now report three experiments to examine how the time available for processing target information influences trajectory characteristics in two-degree-of-freedom forces and multijoint movements. We first sought to determine whether the specification of force direction could also take the form of a discrete stochastic process in pulses of wrist muscle force, where direction can vary continuously. With four equiprobable targets (two force amplitudes in each of two directions separated by 22 degrees or 90 degrees), amplitude was specified from a central default value for both narrow and wide target separations as a continuous variable. Direction, however, remained specified as a discrete variable for wide target separations. For narrow target separations, the directional distribution of default responses suggested the presence of both discrete and central values. We next examined point-to-point movements in a multijoint planar hand movement task with targets at two distances and two directions but at five directional separations (from 30 degrees to 150 degrees separation). We found that extent was again specified continuously from a central default. Direction was specified discretely from alternative default directions when target separation was wide and continuously from a central default when separation was narrow. The specification of both extent and direction evolved over a 200-ms time period beginning about 100 ms after target presentation. As in elbow force pulses, extent was specified progressively in both correct and wrong direction responses through a progressive improvement in the scaling of acceleration and velocity peaks to the target. On the other hand, movement time and hand path straightness did not change significantly in the course of specification. Thus, the specification of movement time and linearity, global features of the trajectories, are given priority over the specific values of extent and direction. In a third experiment, we varied the distances between unidirectional target pairs and found that movement extent is specified discretely, like direction, when the disparity in distances is large. The implications of these findings for contextual effects on trajectory planning are discussed. The independence of extent and direction specification and the prior setting of response duration and straightness provide critical support for the hypothesis that point-to-point movements are planned vectorially.

Recent work has produced methods to solve the winding-constrained optimal feedback navigation problem. Given the start and the goal positions and the winding constraints, the solution to this problem is a feedback vector field such that, when integrated from the start, the trajectory is the shortest path connecting the start and the goal which satisfies given constraints. Such constraints intuitively restrict the direction and the number of times the path winds around given planar regions. We formulate a continuous version of this problem that contrasts with the discrete treatments previously presented. This leads to a geometrical characterization of the problem for which simplicial complex approximation is particularly useful. Thus, it yields theoretical insight as well as a practical algorithm for approximating the continuous problem using an efficient and high-accuracy heuristic-driven front propagation method on simplicial meshes. Experimental results are given evaluating the solution quality and efficiency of the method versus methods based on the discrete formulation and without using heuristics.

For many companies, the world seems to be moving too fast to plan even one year ahead. By focusing simply on annual goals; management risks navigating the enterprise in the wrong direction due to its failure to take important information beyond the fiscal year’s end adequately into account

When considering the necessary role automation must play in future space missions and endeavors, it is important to study basic scenarios that further our understanding of the challenges we must overcome to meet such objectives. We believe some of these challenges lie in the inherent disconnect between the planning of tasks and the development of the continuous trajectories that must be followed to accomplish these tasks. While terrestrial action choices to optimize fuel use and time can be implemented with traditional AI planning tools, the optimization of these resources with respect to space operations requires full-state trajectories -i.e., geometric motion (a path) that includes point velocities/accelerations governed by nonlinear differential equations of motion. The difficulty in this is perhaps no where more apparent than in space-based assembly with self-actuated components that rendezvous/dock, as the action choices for each component and its corresponding trajectories scale with the number of assembly members. By integrating optimal task planning with specialized astrodynamics "experts" for optimal orbit transfer and proximity docking operations, we show that a trajectory planning library with an application to self-assembly can be developed that is both capable and efficient.

Industrial companies change the paradigms of business operations from optimisation of manufacturing processes to optimisation of products life cycles. This is done in order to activate the value of products, taking into account the potentials of product services in all phases of each product's life. For this paradigm it is necessary to develop strategies, methods and technologies to manage the business processes and the information and knowledge required in all phases of a product´s life. The paper descirbes the different views and requirements for a holistic life cycle management and a general classification framework developed in a working group of the network of excellence "knowledge community in production" (KCiP) funded by EC. The paper also presents the results of a study undertaken among European academic research institutes in this field which identifies current research approaches and derives future demands.

This paper discusses the use of intelligent planning as a control mechanism for agents situated in a virtual world. Virtual environments require a planning system that is flexible enough to handle situations impossible to predict before execution has started. In order to produce believable results, the planner should also be able to model and handle emotions. Working towards this direction, the authors propose a motivation-driven, continuous hierarchical planner as an appropriate paradigm.

Existing Unmanned Aerial Vehicle (UAV) systems exhibit shortcomings in providing continuous, responsive, timely, and detailed information and targeting support to Army tactical commander's combat operations in an Army XXI battlespace. To synchronize Tactical UAV (TUAV) missions with supported operations, time is the critical element. Anything that can reduce TUAV planning time, while maintaining plan effectiveness, will expedite execution of a TUAV1s mission. Autonomous flight with some ability to avoid and evade certain threats would increase survivability further. This paper begins by presenting some of the recent successes achieved by artificial intelligence (Al) planners and schedulers on complex real-world problems. It then attempts to show how NASA's demonstrated utility of a dynamic Al planning system prototype for conducting autonomous distributed planning and execution for a team of rovers engaged in missions to achieve science goals during planetary operations can be generalized and applied to a team of TUAVs. Last, it discusses some data collection opportunities that should appear due to the ability to place increasingly more processing and data storage capabilities onboard TUAVs and some of the key challenges to use those capabilities to produce more timely and immediately usable interpretations.