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High product diversity, dynamic market conditions, and a lack of skilled workers are current challenges in manufacturing. Industrial robots autonomously planning and completing upcoming production tasks can help companies address these challenges. In this publication, we focus on autonomous task planning within industrial robotics and investigate how to facilitate the use of automated planning techniques from the field of artificial intelligence for this purpose. First, we present a novel methodology to automatically adapt abstractly modeled planning domains to the characteristics of individual application cases a user intends to implement. A planning domain is a formalized representation of the robot's working environment that builds the basis for automated planning. Second, we integrate this approach into the procedure for developing skills-based industrial robotic applications to enable them to perform autonomous task planning. Finally, we demonstrate the use of the methodology within the application field kitting in two reference scenarios with a mobile robot and a stationary robot cell. Using our methodology, persons without expertise in automated planning can enable a robot for autonomous task planning without much extra effort. [ABSTRACT FROM AUTHOR]

Purposes: This study aimed to develop and validate algorithms for automating intensity modulated radiation therapy (IMRT) planning in breast cancer patients, with a focus on patient anatomical characteristics. Material and Methods: We retrospectively selected 400 breast cancer patients without lymph node involvement for automated treatment planning. Automation was achieved using the Eclipse Scripting Application Programming Interface (ESAPI) integrated into the Eclipse Treatment Planning System. We employed three beam insertion geometries and three optimization strategies, resulting in 3600 plans, each delivering a 40.05 Gy dose in 15 fractions. Gantry angles in the tangent fields were selected based on a criterion involving the minimum intersection area between the Planning Target Volume (PTV) and the ipsilateral lung in the Beam's Eye View projection. ESAPI was also used to gather patient anatomical data, serving as input for Random Forest models to select the optimal plan. The Random Forest classification considered both beam insertion geometry and optimization strategy. Dosimetric data were evaluated in accordance with the Radiation Therapy Oncology Group (RTOG) 1005 protocol. Results: Overall, all approaches generated high‐quality plans, with approximately 94% meeting the acceptable dose criteria for organs at risk and/or target coverage as defined by RTOG guidelines. Average automated plan generation time ranged from 6 min and 37 s to 9 min and 22 s, with the mean time increasing with additional fields. The Random Forest approach did not successfully enable automatic planning strategy selection. Instead, our automated planning system allows users to choose from the tested geometry and strategy options. Conclusions: Although our attempt to correlate patient anatomical features with planning strategy using machine learning tools was unsuccessful, the resulting dosimetric outcomes proved satisfactory. Our algorithm consistently produced high‐quality plans, offering significant time and efficiency advantages. [ABSTRACT FROM AUTHOR]

Automated planning is a prominent Artificial Intelligence (AI) challenge that has been extensively studied for decades, which has led to the development of powerful domain-independent planning systems. The performance of domain-independent planning systems are strongly affected by the structure of the search space, that is dependent on the application domain and on its encoding. This paper proposes and investigates a novel way of combining machine learning and heuristic search to improve domain-independent planning. On the learning side, we use learning to predict the plan cost of a good solution for a given instance. On the planning side, we propose a bound-sensitive heuristic function that exploits such a prediction in a state-space planner. Our function combines the input prediction (derived inductively) with some pieces of information gathered during search (derived deductively). As the prediction can sometimes be grossly inaccurate, the function also provides means to recognise when the provided information is actually misguiding the search. Our experimental analysis demonstrates the usefulness of the proposed approach in a standard heuristic best-first search schema. [ABSTRACT FROM AUTHOR]

Artificial-intelligence (AI) researchers have made very considerable advances in their theoretical knowledge of planning over the past few decades. But the impact of AI on society in the coming years will depend on how much these discoveries improve the real-world performance of automated planning, or AP, an AI subfield that seeks to create computer programs that can generate plans to achieve a particular goal. If practical applications of automated planning continue to stagnate, it could hold back all of AI, even as its other subfields continue to mature. Modest progress, meanwhile, would facilitate modest economic and military uses of artificial intelligence. And should AP experience the same kind of spectacular breakout as reinforcement learning, which is being used practically in a wide variety of fields, from robotics to finance, the peril and promise of artificial intelligence might be fully realized. [ABSTRACT FROM AUTHOR]

Human-aware planning involves generating plans that are explicable, i.e. conform to user expectations, as well as providing explanations when such plans cannot be found. In this paper, we bring these two concepts together and show how an agent can achieve a trade-off between these two competing characteristics of a plan. To achieve this, we conceive a first-of-its-kind planner MEGA that can reason about the possibility of explaining a plan in the plan generation process itself. We will also explore how solutions to such problems can be expressed as "self-explaining plans" – and show how this representation allows us to leverage classical planning compilations of epistemic planning to reason about this trade-off at plan generation time without having to incur the computational burden of having to search in the space of differences between the agent model and the mental model of the human in the loop in order to come up with the optimal trade-off. We will illustrate these concepts in two well-known planning domains, as well as with a robot in a typical search and reconnaissance task. Human factor studies in the latter highlight the usefulness of the proposed approach. [ABSTRACT FROM AUTHOR]

Automated planning for problems without an explicit model is an elusive research challenge. However, if tackled, it could provide a general approach to problems in real-world unstructured environments. There are currently two strong research directions in the area of artificial intelligence (AI), namely machine learning and symbolic AI. The former provides techniques to learn models of unstructured data but does not provide further problem solving capabilities on such models. The latter provides efficient algorithms for general problem solving, but requires a model to work with. Creating the model can itself be a bottleneck of many problem domains. Complicated problems require an explicit description that can be very costly or even impossible to create. In this paper, we propose a combination of the two areas, namely deep learning and classical planning, to form a planning system that works without a human-encoded model for variably scaled problems. The deep learning part extracts the model in the form of a transition system and a goal-distance heuristic estimator; the classical planning part uses such a model to efficiently solve the planning problem. Both networks in the planning system, we introduced, work with a problem in its graphic form and there is no need for any additional information to create the state transition system or to estimate a heuristic value. We proposed three different architectures for the heuristic estimator to compare different characteristics of well- known deep learning techniques. Besides the design of such planning systems, we provide experimental evaluation comparing the implemented techniques to classical model-based methods. [ABSTRACT FROM AUTHOR]

Purpose: To develop a knowledge‐based automated planning pipeline that generates treatment plans without feature engineering, using deep neural network architectures for predicting three‐dimensional (3D) dose. Methods: Our knowledge‐based automated planning (KBAP) pipeline consisted of a knowledge‐based planning (KBP) method that predicts dose for a contoured computed tomography (CT) image followed by two optimization models that learn objective function weights and generate fluence‐based plans, respectively. We developed a novel generative adversarial network (GAN)‐based KBP approach, a 3D GAN model, which predicts dose for the full 3D CT image at once and accounts for correlations between adjacent CT slices. Baseline comparisons were made against two state‐of‐the‐art deep learning–based KBP methods from the literature. We also developed an additional benchmark, a two‐dimensional (2D) GAN model which predicts dose to each axial slice independently. For all models, we investigated the impact of multiplicatively scaling the predictions before optimization, such that the predicted dose distributions achieved all target clinical criteria. Each KBP model was trained on 130 previously delivered oropharyngeal treatment plans. Performance was tested on 87 out‐of‐sample previously delivered treatment plans. All KBAP plans were evaluated using clinical planning criteria and compared to their corresponding clinical plans. KBP prediction quality was assessed using dose‐volume histogram (DVH) differences from the corresponding clinical plans. Results: The best performing KBAP plans were generated using predictions from the 3D GAN model that were multiplicatively scaled. These plans satisfied 77% of all clinical criteria, compared to the clinical plans, which satisfied 67% of all criteria. In general, multiplicatively scaling predictions prior to optimization increased the fraction of clinical criteria satisfaction by 11% relative to the plans generated with nonscaled predictions. Additionally, these KBAP plans satisfied the same criteria as the clinical plans 84% and 8% more frequently as compared to the two benchmark methods, respectively. Conclusions: We developed the first knowledge‐based automated planning framework using a 3D generative adversarial network for prediction. Our results, based on 217 oropharyngeal cancer treatment plans, demonstrated superior performance in satisfying clinical criteria and generated more realistic plans as compared to the previous state‐of‐the‐art approaches. [ABSTRACT FROM AUTHOR]

Where most so-called autonomous underwater vehicles are automated, they have limited autonomy in the traditional sense of being free to choose new courses of action. Onboard software systems have a narrow range of sensor processing and mission replanning capabilities due to the expense and the complexity of these operations, as well as a lack of operator trust in the validity of plans that operators cannot inspect before execution. The advent of machine learning and the maturation of new ideas in automated planning will reduce the cost and difficulty of fielding more autonomous systems, attacking the first difficulty. The second difficulty--that of trust--is severe for underwater robotics, where the more traditional approach of intelligent systems acting as operator aids is made difficult by the physics. Increasing trust will require technical advances in assured autonomy that are only just beginning. We survey the history of artificial intelligence (AI) for robotics, highlighting important developments that will influence future systems. [ABSTRACT FROM AUTHOR]

Automated planning is a well-established field of artificial intelligence (AI), with applications in route finding, robotics and operational research, among others. The task of developing a plan is often solved by finding a path in a graph representing the search domain; a robust plan consists of numerous paths that can be chosen if the execution of the best (optimal) one fails. While robust planning for a single entity is rather simple, development of a robust plan for multiple entities in a common environment can lead to combinatorial explosion. This paper proposes a novel hybrid approach, joining heuristic search and the wavefront algorithm to provide a plan featuring robustness in areas where it is needed, while maintaining a low level of computational complexity.

A smart home needs to be human-centric, where it tries to fulfill human needs given the devices it has. Various works are developed to provide homes with reasoning and planning capability to fulfill goals, but most do not support complex sequence of plans or require significant manual effort in devising subplans. This is further aggravated by the need to optimize conflicting personal goals. A solution is to solve the planning problem represented as constraint satisfaction problem (CSP). But CSP uses hard constraints and, thus, cannot handle optimization and partial goal fulfillment efficiently. This paper aims to extend this approach to weighted CSP. Knowledge representation to help in generating planning rules is also proposed, as well as methods to improve performances. Case studies show that the system can provide intelligent and complex plans from activities generated from semantic annotations of the devices, as well as optimization to maximize personal constraints’ fulfillment. Note to Practitioners—Smart home should maximize the fulfillment of personal goals that are often conflicting. For example, it should try to fulfill as much as possible the requests made by both the mother and daughter who wants to watch TV but both having different channel preferences. That said, every person has a set of goals or constraints that they hope the smart home can fulfill. Therefore, human-centric system that automates the loosely coupled devices of the smart home to optimize the goals or constraints of individuals in the home is developed. Automated planning is done using converted services extracted from devices, where conversion is done using existing tools and concepts from Web technologies. Weighted constraint satisfaction that provides the declarative approach to cover large problem domain to realize the automated planner with optimization capability is proposed. Details to speed up planning through search space reduction are also given. Real-time case studies are run in a prototype smart home to demonstrate its applicability and intelligence, where every planning is performed under a maximum of 10 s. The vision of this paper is to be able to implement such system in a community, where devices everywhere can cooperate to ensure the well-being of the community. [ABSTRACT FROM AUTHOR]

We present a technique for automatically extracting mutual exclusion invariants from temporal planning instances. It first identifies a set of invariant templates by inspecting the lifted representation of the domain and then checks these templates against properties that assure invariance. Our technique builds on other approaches to invariant synthesis presented in the literature but departs from their limited focus on instantaneous actions by addressing temporal domains. To deal with time, we formulate invariance conditions that account for the entire temporal structure of the actions and the possible concurrent interactions between them. As a result, we construct a more comprehensive technique than previous methods, which is able to find not only invariants for temporal domains but also a broader set of invariants for sequential domains. Our experimental results provide evidence that our domain analysis is effective at identifying a more extensive set of invariants, which results in the generation of fewer multi-valued state variables. We show that, in turn, this reduction in the number of variables reflects positively on the performance of the temporal planners that use a variable/value representation. [ABSTRACT FROM AUTHOR]

Many robotic control architectures perform a continuous cycle of sensing, reasoning and acting, where that reasoning can be carried out in a reactive or deliberative form. Reactive methods are fast and provide the robot with high interaction and response capabilities. Deliberative reasoning is particularly suitable in robotic systems because it employs some form of forward projection (reasoning in depth about goals, pre-conditions, resources and timing constraints) and provides the robot reasonable responses in situations unforeseen by the designer. However, this reasoning, typically conducted using Artificial Intelligence techniques like Automated Planning (AP), is not effective for controlling autonomous agents which operate in complex and dynamic environments. Deliberative planning, although feasible in stable situations, takes too long in unexpected or changing situations which require re-planning. Therefore, planning cannot be done on-line in many complex robotic problems, where quick responses are frequently required. In this paper, we propose an alternative approach based on case-based policy learning which integrates deliberative reasoning through AP and reactive response time through reactive planning policies. The method is based on learning planning knowledge from actual experiences to obtain a case-based policy. The contribution of this paper is two fold. First, it is shown that the learned case-based policy produces reasonable and timely responses in complex environments. Second, it is also shown how one case-based policy that solves a particular problem can be reused to solve a similar but more complex problem in a transfer learning scope. [ABSTRACT FROM AUTHOR]

In the context of deliberative reasoning, autonomous systems should be able to explicitly reason about goals. Most research in automated planning assume that goals are initially given and fixed and planners generate plans to achieve them. However, in some real-world scenarios where planners work in an on-line continual planning setting, additional goals may arrive over time. When information about future goal arrivals is known, it can be exploited to direct the system towards those goals even though they have not arrived yet. Recent work presented an approach that exhibited such anticipatory behavior based on hindsight optimization on a domain-dependent setup. In this paper, we tackle this problem from the point of view of domain-independent planning. The available per-step time in on-line continual problems can be very short and domain-independent planners can scale poorly in such short time. In fact, a domain-independent reimplementation of the hindsight optimization scheme may not even be applicable. We propose several alternative approaches that consider future goals in a domain-independent planning process. Experimental results in several benchmark domains suggest that these approaches exhibit a more efficient and effective behavior than a reactive approach in which goals are pursued after their arrival. [ABSTRACT FROM AUTHOR]

Automated Planning deals with reasoning processes where a set of goals must be achieved from an initial state using some actions. Most work on planning have a static view of goals; they are given at start of the planning process and they do not change over planning and/or plan execution. However, in many real world domains, agents need to consider dynamic goal management. In this paper, we propose to increase the performance of planning agents by learning when goals will appear in the near future. The learned predictive models allow agents to perform some kind of anticipatory planning, where the planning process considers not only current goals, but also future predicted goals. We also study under which conditions this anticipatory approach outperforms a standard planning approach. Finally, experiments that support our hypothesis are presented. [ABSTRACT FROM AUTHOR]

To build intelligent manufacturing systems that react flexibly in case of failures or unexpected circumstances, manufacturing capabilities of production systems must be utilized as much as possible. Artificial Intelligence (AI) and, in particular, automated planning can contribute to this by enabling flexible production processes. To efficiently leverage automated planning, an almost complete planning domain description of the real-world is necessary. However, creating such planning descriptions is a demanding and error-prone task that requires high manual efforts even for domain experts. In addition, maintaining the encoded knowledge is laborious and, thus, can lead to outdated domain descriptions. To reduce the high efforts, already existing knowledge can be reused and transformed automatically into planning descriptions to benefit from organization-wide knowledge engineering activities. This paper presents a novel approach that reduces the described efforts by reusing existing knowledge for planning and scheduling in Industry 4.0 (I4.0). For this purpose, requirements for developing a converter that transforms existing knowledge are derived from literature. Based on these requirements, the SWS2PDDL converter is developed that transforms the knowledge into formal Planning Domain Definition Language (PDDL) descriptions. The approach's usefulness is verified by a practical evaluation with a near real-world application scenario by generating failures in a physical smart factory and evaluating the generated re-planned production processes. When comparing the resulting plan quality to those achieved by using a manually modeled planning domain by a domain expert, the automatic transformation by SWS2PDDL leads to comparable or even better results without requiring the otherwise high manual modeling efforts. • Literature-based requirements for transforming semantic models into planning domains. • Literature-based requirements for using AI planning in business process management. • Applying design science for developing a converter for reusing semantic models. • Using AI planners to manually created and automatically converted planning domains. • Evaluation on a smart factory for verifying the suitability of the converter. [ABSTRACT FROM AUTHOR]

In many cases several entities, such as commercial companies, need to work together towards the achievement of joint goals, while hiding certain private information. To collaborate effectively, some sort of plan is needed to coordinate the different entities. We address the problem of automatically generating such a coordination plan while preserving the agents' privacy. Maintaining privacy is challenging when planning for multiple agents, especially when tight collaboration is needed and a global high-level view of the plan is required. In this work we present the Greedy Privacy-Preserving Planner (GPPP), a privacy preserving planning algorithm in which the agents collaboratively generate an abstract and approximate global coordination plan and then individually extend the global plan to executable plans. To guide GPPP, we propose two domain independent privacy preserving heuristics based on landmarks and pattern databases, which are classical heuristics for single agent search. These heuristics, called privacy-preserving landmarks and privacy preserving PDBs, are agnostic to the planning algorithm and can be used by other privacy-preserving planning algorithms. Empirically, we demonstrate on benchmark domains the benefits of using these heuristics and the advantage of GPPP over existing privacy preserving planners for the multi-agent STRIPS formalism. [ABSTRACT FROM AUTHOR]

Traveling recommendation systems have become very popular applications for organizing and planning tourist trips. Among other challenges, these applications are faced with the task of maintaining updated information about popular tourist destinations, as well as providing useful tourist guides that meet the users preferences. In this work we present the PlanTour , a system that creates personalized tourist plans using the human-generated information gathered from the minube 1 1 www.minube.com traveling social network. The system follows an automated planning approach to generate a multiple-day plan with the most relevant points of interest of the city/region being visited. Particularly, the system collects information of users and points of interest from minube , groups these points with clustering techniques to split the problem into per-day sub-problems. Then, it uses an off-the-shelf domain-independent automated planner that finds good quality tourist plans. Unlike other tourist recommender systems, the PlanTour planner is able to organize relevant points of interest taking into account user’s expected drives, and user scores from a real social network. The paper also highlights how to use human provided recommendations to guide the search for solutions of combinatorial tasks. The resulting intelligent system opens new possibilities of combining human-generated knowledge with efficient automated techniques when solving hard computational tasks. From an engineering perspective we advocate for the use of declarative representations of problem solving tasks that have been shown to improve modeling and maintenance of intelligent systems. [ABSTRACT FROM AUTHOR]

The application of intelligent systems in industrial environments is still restricted, although autonomous systems based on artificial intelligence (AI) techniques have been under development for over 50 years and spreading into several areas since the last decade. Such integration goes against cultural and technical factors found on the shop floor and its project is often challenging. Classical industrial systems make use of programmable logic controllers (PLCs), which centralise control by using a simple and low-level language. Automated planning solutions use a formal language called planning domain definition language (PDDL) to address specific problems, with predefined initial and target scenarios. Such feature seems to be interesting in some cases, since the generic domain models cannot predict all possible scenarios in a domain. Therefore, the applicability of intelligent systems in industrial cases is directly linked to the development of applications that allow integrating different languages, thus ensuring the access to PLCs and analysing specific situations. The current paper presents an application that integrates automated planning solution and PLCs as well as analyses such integrations’ functionality. Preliminary results are shown by using object process control (OPC) and TCP/IP client/server approaches. A comparison with classical approaches was presented for a specific case study. [ABSTRACT FROM AUTHOR]

In recent years the field of Automated Planning has significantly advanced and several powerful domain-independent planners have been developed. However, none of these systems clearly outperforms all the others in every known benchmark domain. This observation motivated the idea of configuring and exploiting a portfolio of planners to perform better than any individual planner: some recent planning systems based on this idea achieved significantly good results in experimental analysis and International Planning Competitions. Such results let us suppose that future challenges of the Automated Planning community will converge on designing different approaches for combining existing planning algorithms. This paper reviews existing techniques and provides an exhaustive guide to portfolio-based planning. In addition, the paper outlines open issues of existing approaches and highlights possible future evolution of these techniques. [ABSTRACT FROM AUTHOR]

Automated planning is a well-established field of artificial intelligence (AI), with applications in route finding, robotics and operational research, among others. The task of developing a plan is often solved by finding a path in a graph representing the search domain; a robust plan consists of numerous paths that can be chosen if the execution of the best (optimal) one fails. While robust planning for a single entity is rather simple, development of a robust plan for multiple entities in a common environment can lead to combinatorial explosion. This paper proposes a novel hybrid approach, joining heuristic search and the wavefront algorithm to provide a plan featuring robustness in areas where it is needed, while maintaining a low level of computational complexity. [ABSTRACT FROM AUTHOR]

Deterministic domain-independent multiagent planning is an approach to coordination of cooperative agents with joint goals. Provided that the agents act in an uncertain and dynamic environment, such plans can fail. The straightforward approach to recover from such situations is to compute a new plan from scratch, that is to replan. Even though, in a worst case, plan repair or plan re-use does not yield an advantage over replanning from scratch, there is a sound evidence from practical use that approaches trying to repair the failed original plan can outperform replanning in selected problems. One of the possible plan repairing techniques is based on preservation of fragments of the older plans. This work theoretically analyses complexity of plan repairing approaches based on preservation of fragments of the original plan and experimentally studies three practical aspects affecting its efficiency in various multiagent settings. We focus both on the computational, as well as the communication efficiency of plan repair in comparison to replanning from scratch and we report on the influence of the following properties on the efficiency of plan repair: (1) the number of involved agents in the plan repairing process, (2) inter-dependencies among the repaired actions, and finally (3) particular modes of re-use of the older plans. [ABSTRACT FROM AUTHOR]

We describe a planning algorithm, NDP2, that finds strong-cyclic solutions to nondeterministic planning problems by using a classical planner to solve a sequence of classical planning problems. NDP2 is provably correct, and fixes several problems with prior work. We also describe two preprocessing algorithms that can provide a restricted version of the symbolic abstraction capabilities of the well-known MBP planner. The preprocessing algorithms accomplish this by rewriting the planning problems, hence do not require any modifications to NDP2 or its classical planner. In our experimental comparisons of NDP2 (using FF as the classical planner) to MBP in six different planning domains, each planner outperformed the other in some domains but not others. Which planner did better depended on three things: the amount of nondeterminism in the planning domain, domain characteristics that affected how well the abstraction techniques worked, and whether the domain contained unsolvable states. [ABSTRACT FROM AUTHOR]

Abstract: Web services as independent software components are published by service providers over the Internet and invoked by service requesters for their desired functionalities. In many cases, however, there is no single service in a Web service repository satisfying a service request. So how to design an efficient method for composing a chain of connected services has become an important research issue. Recently, much research has been done into the search time reduction when finding a composite service. However, most methods take a long time for traversing all of the Web services in a service repository, thus it makes their response time significantly overrun a user’s waiting patience. This paper develops an efficient approach for automatic composition of Web services using the state-of-the-art Artificial Intelligence (AI) planners, where a Web service composition (WSC) problem is regarded as a WSC planning problem. Unlike most traditional WSC methods that traverse a Web service repository many times, our approach converts a Web service repository into a planning domain in PDDL just once, which will only be regenerated when the Web service repository changes. This treatment substantially reduces the response time and improves the scalability of solving WSC problems. We have implemented a prototype system and conducted extensive experiments on large-scale Web service repositories. The experimental results demonstrate that our proposed approach outperforms the state-of-the-art. [Copyright &y& Elsevier]

Abstract: When calibrating a machine tool, multiple measurement tasks will be performed, each of which has an associated uncertainty of measurement. International Standards and best-practice guides are available to aid with estimating uncertainty of measurement for individual tasks, but there is little consideration for the temporal influence on the uncertainty when considering interrelated measurements. Additionally, there is an absence of any intelligent method capable of optimising (reducing) the estimated uncertainty of the calibration plan as a whole. In this work, the uncertainty of measurement reduction problem is described and modelled in a suitable language to allow state-of-the-art artificial intelligence planning tools to produce optimal calibration plans. The paper describes how the continuous, non-linear temperature aspects are discretized and modelled to make them easier for the planner to solve. In addition, detail is provided as how the complex uncertainty equations are modelled in a restrictive language where its syntax heavily influences the encoding. An example is shown for a three-axis machine, where the produced plan exhibits intelligent behaviour in terms of scheduling measurements against temperature deviation and the propagation of error uncertainties. In this example, a reduction of 58% in the estimated uncertainty of measurement due to intelligently scheduling a calibration plan is observed. This reduction in the estimated uncertainty of measurement will result in an increased conformance zone, thus reducing false acceptance and rejection of work-pieces. [Copyright &y& Elsevier]

The CIRCA planning system automatically creates reactive plans and uses formal verification techniques to prove that those plans will preserve system safety. CIRCA's timed automata verification system is highly efficient, yet can display pathologically bad behavior when reasoning about reaction loops, a particular form of interacting cycles of states. In this paper, we describe a loop acceleration technique that recognizes these state-space structures during the verification process and bypasses the process of expanding an arbitrarily large cycle of states, effectively compressing loops of arbitrary size into a compact, finite set of states. The resulting performance improvement can be very dramatic: in domains where tight loops of short-duration transitions interact with long-duration transitions, our new loop acceleration methods can reduce verification time (and hence planning time) from hours to below a second. [ABSTRACT FROM AUTHOR]

Abstract: The utility of including loops in plans has been long recognized by the planning community. Loops in a plan help increase both its applicability and the compactness of its representation. However, progress in finding such plans has been limited largely due to lack of methods for reasoning about the correctness and safety properties of loops of actions. We present novel algorithms for determining the applicability and progress made by a general class of loops of actions. These methods can be used for directing the search for plans with loops towards greater applicability while guaranteeing termination, as well as in post-processing of computed plans to precisely characterize their applicability. Experimental results demonstrate the efficiency of these algorithms. We also discuss the factors which can make the problem of determining applicability conditions for plans with loops incomputable. [Copyright &y& Elsevier]

Project management tasks, such as productivity monitoring and cost estimation, require data to be fused from multiple data sources. Data fusion approaches incorporated in the existing research studies within the construction management domain support a specific task or a decision (e.g., labor productivity monitoring or defect detection). Hence, most of the previously developed approaches do not necessarily support tasks other than the ones that they were intended for. This paper describes an automated planning approach as a general way to fuse data from multiple sources to support construction productivity monitoring tasks. A prototype system, which incorporates two planning algorithms, was developed to validate the generality of the approach on the basis of representative queries of construction engineers and managers identified in previous research studies. [ABSTRACT FROM AUTHOR]

Abstract: The automatic generation of individualized plans in specific domains is an open problem that combines aspects related to automated planning, machine learning or recommendation systems technology. In this paper, we focus on a specific instance of that task; that of generating e-learning courses adapted to students’ profiles, within the automated planning paradigm. One of the open problems in this type of automated planning application relates to what is known as oversubscription: given a set of goals, each one with a utility, obtain a plan that achieves some (or all) the goals, maximizing the utility, as well as minimizing the cost of achieving those goals. In the generation of e-learning designs there is only one goal: generating a course design for a given student. However, in order to achieve the goal, the course design can include many different kinds of activities, each one with a utility (that depend on the student profile) and cost. Furthermore, these activities are usually grouped into clusters, so that at least one of the activities in each cluster is needed, though many more can be used. Finally, there is also an overall cost threshold (usually in terms of student time). In this paper, we present our work on building an individualized e-learning design. We pose each course design as a variation of the oversubscription problem, that we call the clustered-oversubscription problem, and we use linear programming for assisting a planner to generate the design that better adapts to the student. [Copyright &y& Elsevier]