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This paper proposes an interactive search approach, termed INTERACT, which couples sampling-based motion planning with action planning in order to effectively solve the combined task and motion planning problem. INTERACT is geared towards scenarios involving a mobile robot operating in a fully known environment consisting of static and movable objects. INTERACT makes it possible to specify a task in the planning domain definition language (PDDL) and automatically computes a collision-free and dynamically feasible trajectory that enables the robot to accomplish the task. The coupling of sampling-based motion planning with action planning is made possible by expanding a tree of feasible motions and partitioning it into equivalence classes based on the task predicates. Action plans provide guidance as to which a equivalence class should be further expanded. Information gathered during the motion tree expansion is used to adjust the action costs in order to effectively guide the expansion towards the goal. This interactive process of selecting an equivalence class, expanding the motion tree to implement its action plan and updating the action costs and plans to reflect the progress made is repeated until a solution is found. Experimental validation is provided in simulation using a robotic vehicle to accomplish sophisticated pick-and-place tasks. Comparisons to previous work show significant improvements. [ABSTRACT FROM PUBLISHER]

Domain-Independent planning is known to be a very hard search problem, and in the last three decades many search techniques and heuristics have been developed with the aim of efficiently solving such a task. These techniques and heuristics include the usage of landmarks, which are logical expressions consisting of facts that become true or actions that are executed in any solution plan. We propose the usage of landmarks for speeding up the search of the planner LPG, a system implementing a planning approach based on the use of local search in the space of the action graphs of the planning problem. The results of an experimental evaluation of the proposed techniques show that these techniques can improve the performance of LPG, obtaining a planning system that performs similarly to the state-of-the-art planner LAMA. Moreover, we introduce and experimentally evaluate the concept of "quasi-landmarks"; these are facts that are likely to become true in every solution plan, or facts that must become true in a subset of the solution plans. [ABSTRACT FROM AUTHOR]

One of the challenges to build a smart city is to create intelligent and ubiquitous geospatial Web services, and provide a composition of these services available to users. These geospatial Web services must be better-tuned to a given context. Using AI planning techniques and semantic enhancement, this paper presents a dynamic, context-aware service composition method which is achieved by transforming the service composition problem into a planning problem described in a standardized fashion using PDDL. Semantic representation of a geospatial Web service is modeled by extending OWL-S ontology with GeoContext class, GeoContextPrecondition class, GeoContextEffect class and GeoContextBinding class, which support geo-context and geo-context adaptation. Semantic information is used for the enhancement of the composition process as well as for approximating the optimal composite service when exact solutions are not found. Independence from specific planners is maintained. The generating plan is transformed to a WS-BPEL compatible representation, which is executable on the business process execution engine. A case study about smart travel is also presented to demonstrate the functionality, effectiveness and potential of the approach. [ABSTRACT FROM AUTHOR]

Research in automated planning provides novel insights into service composition and contributes towards the provision of automatic compositions which adapt to changing user needs and environmental conditions. Most of the existing planning approaches to aggregating services, however, suffer from one or more of the following limitations: they are not domain-independent, cannot efficiently deal with numeric-valued variables, especially sensing outcomes or operator inputs, and they disregard recovery from runtime contingencies due to erroneous service behavior or exogenous events that interfere with plan execution. We present the RuGPlanner, which models the planning task as a Constraint Satisfaction Problem. In order to address the requirements put forward by service domains, the RuGPlanner is endowed with a number of special features. These include a knowledge-level representation to model uncertainty about the initial state and the outcome of sensing actions, and efficient handling of numeric-valued variables, inputs to actions or observational effects. In addition, it generates plans with a high level of parallelism, it supports a rich declarative language for expressing extended goals, and allows for continual plan revision to deal with sensing outputs, failures, long response times or timeouts, as well as the activities of external agents. The proposed planning framework is evaluated based on a number of scenarios to demonstrate its feasibility and efficiency in several planning domains and execution circumstances which reflect concerns from different service environments. [ABSTRACT FROM AUTHOR]

Dealing with planning problems with both logical relations and numeric changes in real-world dynamic environments is challenging. Existing numeric planning systems for the problem often discretize numeric variables or impose convex constraints on numeric variables, which harms the performance when solving problems. In this paper, we propose a novel algorithm framework to solve numeric planning problems mixed with logical relations and numeric changes based on gradient descent. We cast the numeric planning with logical relations and numeric changes as an optimization problem. Specifically, we extend syntax to allow parameters of action models to be either objects or real-valued numbers, which enhances the ability to model real-world numeric effects. Based on the extended modeling language, we propose a gradient-based framework to simultaneously optimize numeric parameters and compute appropriate actions to form candidate plans. The gradient-based framework is composed of an algorithmic heuristic module based on propositional operations to select actions and generate constraints for gradient descent, an algorithmic transition module to update states to next ones, and a loss module to compute loss. We repeatedly minimize loss by updating numeric parameters and compute candidate plans until it converges into a valid plan for the planning problem. In the empirical study, we exhibit that our algorithm framework is both effective and efficient in solving planning problems mixed with logical relations and numeric changes, especially when the problems contain obstacles and non-linear numeric effects. [ABSTRACT FROM AUTHOR]

Automated planning, which deals with the problem of generating sequences of actions, is an emerging research topic due to its potentially wide range of real-world application domains. As well as developing and improving planning engines, the acquisition of domain-specific knowledge is a promising way to improve the planning process. Domain-specific knowledge can be encoded into the modelling language that a range of planning engines can accept. This makes encoding domain-specific knowledge planner-independent, and entails reformulating the domain models and/or problem specifications. While many encouraging practical results have been derived from such reformulation methods (e.g. learning macro-actions), little attention has been paid to the theoretical properties such as completeness (keeping solvability of reformulated problems). In this paper, we focus on a special case - removing primitive actions replaced by macro-actions. We provide a theoretical study and come up with conditions under which it is safe to remove primitive actions, so completeness of reformulation is preserved. We extend this study also for planning operators (actions are instances of operators). [ABSTRACT FROM AUTHOR]

Composite models in Decision Support Systems (DSS) are combinations of model functions to solve complex decision problems. They must be executed successfully to obtain the desired results. Unfortunately, faults may happen during its execution due to the dynamic networks or the information asymmetry between developers and users. Therefore, designing effective failure recovery mechanism to ensure the reliability of the composite model execution is essential. Progress has been made in the web service composition field to obtain limited failure recovery capabilities, but not fully applicable to composite models since model services are informational services in essence which do not change the world conditions. This paper proposes a unified framework, integrated with a process ontology and multiple recovery strategies that can provide valuable failure recovery recommendations intelligently. The recovery strategies have been greatly enhanced in providing greater failure recovery capability. Feasibility and efficiency of this framework have been illustrated and tested. [ABSTRACT FROM AUTHOR]

Hierarchies are one of the most common structures used to understand and conceptualise the world. Within the field of Artificial Intelligence (AI) planning, which deals with the automation of world-relevant problems, Hierarchical Task Network (HTN) planning is the branch that represents and handles hierarchies. In particular, the requirement for rich domain knowledge to characterise the world enables HTN planning to be very useful, and also to perform well. However, the history of almost 40 years obfuscates the current understanding of HTN planning in terms of accomplishments, planning models, similarities and differences among hierarchical planners, and its current and objective image. On top of these issues, the ability of hierarchical planning to truly cope with the requirements of real-world applications has been often questioned. As a remedy, we propose a framework-based approach where we first provide a basis for defining different formal models of hierarchical planning, and define two models that comprise a large portion of HTN planners. Second, we provide a set of concepts that helps in interpreting HTN planners from the aspect of their search space. Then, we analyse and compare the planners based on a variety of properties organised in five segments, namely domain authoring, expressiveness, competence, computation and applicability. Furthermore, we select Web service composition as a real-world and current application, and classify and compare the approaches that employ HTN planning to solve the problem of service composition. Finally, we conclude with our findings and present directions for future work. In summary, we provide a novel and comprehensive viewpoint on a core AI planning technique. [ABSTRACT FROM AUTHOR]

This paper presents a new approach to plan high-level manipulation actions for cleaning surfaces in household environments, like removing dirt from a table using a rag. Dragging actions can change the distribution of dirt in an unpredictable manner, and thus the planning becomes challenging. We propose to define the problem using explicitly uncertain actions, and then plan the most effective sequence of actions in terms of time. However, some issues have to be tackled to plan efficiently with stochastic actions. States become hard to predict after executing a few actions, so replanning every few actions with newer perceptions gives the best results, and the trade-off between planning time and plan quality is also important. Finally a learner is integrated to provide adaptation to changes, such as different rag grasps, robots, or cleaning surfaces. We demonstrate experimentally, using two different robot platforms, that planning is more advantageous than simple reactive strategies for accomplishing complex tasks, while still providing a similar performance for easy tasks. We also performed experiments where the rag grasp was changed, and thus the behaviour of the dragging actions, showing that the learning capabilities allow the robot to double its performance with a new rag grasp after a few cleaning iterations. [ABSTRACT FROM AUTHOR]

Web service composition is a significant problem as the number of available web services increases; however, manual composition is not an efficient option. Automated web service composition can be performed using AI Planning techniques, utilizing descriptions of available atomic web services, enhanced with semantic awareness and relaxation. This paper discusses a unified, semantically aware approach, handling both semantic (OWL-S & SAWSDL) and non-semantic (WSDL) web service descriptions. In the first case, ontology analysis is adopted to semantically enhance the planning domains and problems, in order to deal with cases where exact syntactic input-to-output matching is not feasible. In the non-semantic descriptions case, semantic information is acquired utilizing alternative sources such as lexical thesauri. Concept similarity measures are applied and utilized to achieve the desired degree of semantic relaxation. The solution to a web service composition problem is a plan describing the desired composite service. To support the proposed approach, the PORSCE framework has been implemented. The framework is modular, integrating discrete web service description languages and semantic relaxation techniques. Based on the similarity measures suggested in the paper, performance issues are also explored. [ABSTRACT FROM AUTHOR]

Abstract: Applying learning techniques to acquire action models is an area of intense research interest. Most previous work in this area has assumed that there is a significant amount of training data available in a planning domain of interest. However, it is often difficult to acquire sufficient training data to ensure the learnt action models are of high quality. In this paper, we seek to explore a novel algorithm framework, called TRAMP, to learn action models with limited training data in a target domain, via transferring as much of the available information from other domains (called source domains) as possible to help the learning task, assuming action models in source domains can be transferred to the target domain. TRAMP transfers knowledge from source domains by first building structure mappings between source and target domains, and then exploiting extra knowledge from Web search to bridge and transfer knowledge from sources. Specifically, TRAMP first encodes training data with a set of propositions, and formulates the transferred knowledge as a set of weighted formulas. After that it learns action models for the target domain to best explain the set of propositions and the transferred knowledge. We empirically evaluate TRAMP in different settings to see their advantages and disadvantages in six planning domains, including four International Planning Competition (IPC) domains and two synthetic domains. [Copyright &y& Elsevier]

Testing concurrent programs is a challenging problem due to interleaving explosion: even for a fixed set of inputs, there is a huge number of concurrent runs that need to be tested to account for scheduler behavior. Testing all possible schedules is not practical. Consequently, most effective testing algorithms only test a select subset of runs. For example, limiting testing to runs that contain data races or atomicity violations has been shown to capture a large proportion of concurrency bugs. In this paper we present a general approach to concurrent program testing that is based on techniques from artificial intelligence (AI) automated planning. We propose a framework for predicting concurrent program runs that violate a collection of generic correctness specifications for concurrent programs, namely runs that contain data races, atomicity violations, or null-pointer dereferences. Our prediction is based on observing an arbitrary run of the program, and using information collected from this run to model the behavior of the program, and to predict new runs that contain bugs with one of the above noted violation patterns. We characterize the problem of predicting such new runs as an AI sequential planning problem with the temporally extended goal of achieving a particular violation pattern. In contrast to many state-of-the-art approaches, in our approach feasibility of the predicted runs is guaranteed and, therefore, all generated runs are fully usable for testing. Moreover, our planning-based approach has the merit that it can easily accommodate a variety of violation patterns which serve as the selection criteria for guiding search in the state space of concurrent runs. This is achieved by simply modifying the planning goal. We have implemented our approach using state-of-the-art AI planning techniques and tested it within the Penelope concurrent program testing framework []. Nevertheless, the approach is general and is amenable to a variety of program testing frameworks. Our experiments with a benchmark suite showed that our approach is very fast and highly effective, finding all known bugs. [ABSTRACT FROM AUTHOR]

With the rising popularity of Web services due to their potential to enable interoperability between applications implemented on different platforms, new issues far more complex and crucial than interoperability have appeared, related mainly to Web services composition. Indeed assuming that for each service request there exists a single Web service that perfectly satisfies it on its own is rather unrealistic, in many cases composing functionalities offered by different services may be needed to satisfy a client request. However, current processes of Web services composition tend to be static in the sense that such processes are normally generated off-line and manual as it requires human intervention to be managed. Therefore in order to give value added customized services to users, recognition is growing of the need for automation and dynamicity to be taking in consideration while designing systems supporting Web services integration. Since automated dynamic composition process allows to reduce the time spent in building composition schema, to eliminate human errors and to reduce the overall cost of the process. Thus our research contribution lies in the area of automated dynamic composition of Web services. We propose a conceptual approach to compose Web services by combining the Semantic Web technologies, Artificial Intelligent Planning methods, Multi-Agent architecture and Self-healing propriety of Autonomic Computing paradigm. [ABSTRACT FROM AUTHOR]

Highlights: [•] An automatic solution to compose OWSs (OGC web services). [•] OWSs are semantically annotation using RDF and SPARQL. [•] Artificial Intelligence planning is used to compose OWSs. [•] The proposed solution is evaluated through a case-study in evacuation sheltering. [ABSTRACT FROM AUTHOR]

Geospatial web services are widely utilised to disseminate spatial data and processing capability over the internet. Combining geospatial web services and generating composite web services out of atomic services can provide users with a higher level of functionality. This paper presents a solution for the automatic composition of geospatial web services. The solution relies on semantic web services technology and Artificial Intelligence (AI) planning techniques to generate composite geospatial web services. Web Service Modelling Ontology (WSMO) is selected as the underlying semantic web service framework. A neoclassical planning algorithm called Graphplan is used as the AI planner. The applicability of the proposed solution for composing web services is illustrated in a case study. The result shows that the proposed approach is well suited for the automatic composition of web services. The future requirements for adaptation of the approach in real world problems are broadly described. [ABSTRACT FROM AUTHOR]

This paper proposes a novel approach based on the planning-graph to solve the top-k QoS-aware automatic composition problem of semantic Web services. The approach includes three sequential stages: a forward search stage to generate a planning-graph to reduce the search space of the following two stages greatly, an optimal local QoS calculating stage to compute all the optimal local QoS values of services in the planning, and a backward search stage to find the top-K composed services with optimal QoS values according to the planning-graph and the optimal QoS value. In order to validate the approach, experiments are carried out based on the test sets offered by the WS-Challenge competition 2009. The results show that the approach can find the same optimal solution as the champion system from the competition but also can provide more alternative solutions with the optimal QoS for users. [ABSTRACT FROM AUTHOR]

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Description Logic possesses strong knowledge representation and reasoning capabilities and offers logical foundation for Semantic Web ontology languages such as OWL and OWL-S. However, the present implementations of OWL and OWL-S are deficient in semantic modeling for dynamic service composition. They also do not consider user preferences. AI planning possesses a better modeling capability of action state transformations in planning and provides an effective method for solving planning problem and task decomposition. However, it is limited in knowledge representation and reasoning capabilities. Based on merits of Description Logic, OWL-S and AI planning, this paper extends OWL-S model, proposes a service composition mechanism and testifies its feasibility in Description Logic. The results show that this composition mechanism is not only feasible but also helpful for semantic modeling of service composition in Semantic Web. [ABSTRACT FROM AUTHOR]

In recent years, researchers have been building home robots able to interact and work with people. Yet, because of the complicated and independent robot development environments, it is not always easy to share and reuse robot code created by different providers. In this work, we present an ontologybased framework that integrates service oriented computing environments with the standard web interface to develop reusable robotic services. In addition to the service discovery, selection, and composition processes often performed by traditional web services, our work also includes an adaptive mechanism through which the user can iteratively modify composite robotic services to suit his or her needs. The proposed methodology has been implemented and evaluated, and the results show that our framework can be used to build robotic services successfully. [ABSTRACT FROM AUTHOR]

With the pervasive use of mobile devices and the need for ubiquitous computing, the issue of "context" now becomes a hot topic in human computer interaction research and development. Further, the interface of the interaction beyond the desktop is moving from humans vs. computers to humans vs. context-aware environments. This leads the context to constitute an underlying part of service behavior, especially when interaction with end-users is involved, and consequently this demands the context-aware computing systems to be designed to automatically adapt its behavior to changing environment. Thus, pervasive computing applications need to be more autonomous and sensitive to context. However, in real-life systems context information is naturally dynamic, vague and complex, which lead to an inexact match between provided and required service capabilities. In this vision, we propose in this paper a three-phases adaptation approach: firstly we select the suitable services to the current context and we recommend them to the adaptation process, in the service adaptation phase we perform adaptation by using fuzzy sets represented with linguistic variables and membership degrees to define the user's context and the rules for adopting the policies of implementing a service. Finally we deal with the complex requirements of the user by the composition of the obtained adaptable atomics services. [ABSTRACT FROM AUTHOR]

Abstract: Temporal planning embodies aspects of both planning and scheduling. Many temporal planners handle these two subproblems in a loose coupling way. This way simplifies the temporal planning problem but restricts the modeling power. In particular, the simplification fails to handle such temporal planning problems that require concurrency, where actions must execute concurrently to achieve expected effects. For those temporally expressive planning problems, the problem of how to integrate planning with scheduling is emphasized for the sake of both finding a valid plan and further, in an effective way. This paper examines three factors that affect the integrated system''s efficiency: information sharing, computation burden balance and interaction frequency. The approach attributes to designing a set of heuristics. By conducting preliminary experiments, the results show good performance of those heuristics compared with the start-of-the-art planner VHPOP. [Copyright &y& Elsevier]

In this paper, novel ideas are presented for solving the automated web service composition problem. Some of the possible real world problems such as partial observability of the environment, nondeterministic effects of web services and service execution failures are solved through a dynamic planning approach. The proposed approach is based on a novel AI planner that is designed for working in highly dynamic environments under time constraints, namely Simplanner. World altering service calls are done according to the WS-Coordination and WS-Business Activity web service transaction specifications in order to physically recover from failure situations and prevent the undesired side effects of the aborted web service composition efforts. [ABSTRACT FROM AUTHOR]

The new advances in information communications technology (ICT) allow expanding the electronic communication networks into an objective environment. These techniques can be used to help users who have to go to a building with several floors, corridors, and departments to get its right location and orientation. The problem gets more complicated when there are a high number of visitors with temporal constraints. Typically the lack of information and signs to guide the user complicates this task. A typical example is a hospital, where patients have scheduled doctor's appointments and in some cases movement problems (handicaps). A possibility for solving this problem is to equip the building with an intelligent system for user detection and orientation. In this article we present a solution that uses a detection and a location system based on wireless technology and artificial intelligence techniques to plan and inform about the paths the user can follow. [ABSTRACT FROM AUTHOR]

Abstract: In recent years, a number of new heuristic search methods have been developed in the field of automated planning. Enforced hill climbing (EHC) is one such method which has been frequently used in a number of AI planning systems. Despite certain weaknesses, such as getting trapped in dead-ends in some domains, this method is more competitive than several other methods in many planning domains. In order to enhance the efficiency of ordinary enforced hill climbing, a new form of enforced hill climbing, called guided enforced hill climbing, is introduced in this paper. An adaptive branch ordering function is the main feature that guided enforced hill climbing has added to EHC. Guided enforced hill climbing expands successor states in the order recommended by the ordering function. Our experimental results in several planning domains show a significant improvement in the efficiency of the enforced hill climbing method, especially when applied to larger problems. [ABSTRACT FROM AUTHOR]

e-Tourism is a tourist recommendation and planning application to assist users on the organization of a leisure and tourist agenda. First, a recommender system offers the user a list of the city places that are likely of interest to the user. This list takes into account the user demographic classification, the user likes in former trips and the preferences for the current visit. Second, a planning module schedules the list of recommended places according to their temporal characteristics as well as the user restrictions; that is the planning system determines how and when to realize the recommended activities. Having the list of recommended activities organized as an agenda (i.e. an executable plan), is a relevant characteristic that most recommender systems lack. [ABSTRACT FROM AUTHOR]

In a Web service-based distributed environment, individual services must be chained together dynamically to solve a complex real world problem. The Semantic Web Service has shown promise for automatic chaining of Web services. This paper addresses semi-automatic geospatial service chaining through Semantic Web Services-based process planning. Process planning includes three phases: process modeling, process model instantiation and workflow execution. Ontologies and Artificial Intelligence (AI) planning methods are employed in process planning to help a user dynamically create an executable workflow for earth science applications. In particular, the approach was implemented in a common data and service environment enabled by interoperable standards from OGC and W3C. A case study of the chaining process for wildfire prediction illustrates the applicability of this approach. [ABSTRACT FROM AUTHOR]

Workflow management in the grid era faces many challenges both related to workflow modeling and workflow execution. To deal with the challenges, the paper argues that the traditional process-driven workflow management paradigm should be replaced by a goal-driven one and proposes a corresponding implementation approach. The contributions of the paper are as follows: (1) a concept of process pattern is put forward that is an effective means not only for procedural knowledge representation and sharing but also for expertise exchange between domain experts and computer scientists; (2) based on process patterns, a goal-driven workflow framework is designed that incorporates semantic Web technologies and artificial intelligent (AI) planning techniques and that can (semi-)automatically generate a workflow on the fly according to the requirements of users and the running context. [ABSTRACT FROM AUTHOR]

Abstract: We present an any-time concurrent probabilistic temporal planner (CPTP) that includes continuous and discrete uncertainties and metric functions. Rather than relying on dynamic programming our approach builds on methods from stochastic local policy search. That is, we optimise a parameterised policy using gradient ascent. The flexibility of this policy-gradient approach, combined with its low memory use, the use of function approximation methods and factorisation of the policy, allow us to tackle complex domains. This factored policy gradient (FPG) planner can optimise steps to goal, the probability of success, or attempt a combination of both. We compare the FPG planner to other planners on CPTP domains, and on simpler but better studied non-concurrent non-temporal probabilistic planning (PP) domains. We present FPG-ipc, the PP version of the planner which has been successful in the probabilistic track of the fifth international planning competition. [Copyright &y& Elsevier]

Automated composition of Web services or the process of forming new value-added Web services is one of the most promising challenges facing the Semantic Web today. Semantics enables Web service to describe capabilities together with their processes, hence one of the key elements for the automated composition of Web services. In this paper, we focus on the functional level of Web services i.e. services are described according to some input, output parameters semantically enhanced by concepts in a domain ontology. Web service composition is then viewed as a composition of semantic links wherein the latter links refer to semantic matchmaking between Web service parameters (i.e. outputs and inputs) in order to model their connection and interaction. The key idea is that the matchmaking enables, at run time, finding semantic compatibilities among independently defined Web service descriptions. By considering such a level of composition, a formal model to perform the automated composition of Web services i.e. Semantic Link Matrix, is introduced. The latter model is required as a starting point to apply problem-solving techniques such as regression (or progression)-based search for Web service composition. The model supports a semantic context in order to find correct, complete, consistent and robust plans as solutions. In this paper, an innovative and formal model for an AI (Artificial Intelligence) planning-oriented composition is presented. Our system is implemented and interacting with Web services which are dedicated to Telecom scenarios. The preliminary evaluation results showed high efficiency and effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

Legacy application design models, which are still widely used for developing context-aware applications, incur important limitations. Firstly, embedding contextual dependencies in the form of if–then rules specifying how applications should react to context changes is impractical to accommodate the large variety of possibly even unanticipated context types and their values. Additionally, application development is complicated and challenging, as programmers have to manually determine and encode the associations of all possible combinations of context parameters with application behaviour. In this paper we propose a framework for building context aware applications on-demand, as dynamically composed sequences of calls to services. We present the design and implementation of our system, which employs goal-oriented inferencing for assembling composite services, dynamically monitors their execution, and adapts applications to deal with contex- tual changes. We describe the failure recovery mechanisms we have implemented, allowing the deployment of the system in a non-perfect environment, and avoiding the delays inherent in re-discovering a suitable service instance. By means of experimental evaluation in a realistic infotainment application, we demonstrate the potential of the proposed solution an effective, efficient, and scalable approach. [ABSTRACT FROM AUTHOR]

Web services composition techniques are gaining momentum as the opportunity to establish reusable and versatile inter-operability applications. Many researchers propose their composition approach based on planning techniques. We propose our context aware planning method which comprises global planning and local optimization based on context information. The major technical contributions of this paper are: (1) We propose an ontology-based framework for the context-aware composition of Web services. Context model, which are structured based on OWL-S, captures the Service-related, Environment-related, and User-related context and can be used in an unambiguous, machine interpretable form. (2) We propose context-aware plan architecture and thus is more scalability and flexibility for the planning process, and thereby improving the efficiency and precision. (3) We propose a hybrid approach to build a plan corresponding to a context-aware service composition, based on global planning and local optimization, considering both the usability and adoption. We test our approach on a simple, yet realistic example, and the preliminary results demonstrate that our implementation provides a practical solution. [ABSTRACT FROM AUTHOR]

Business processes that span organizational borders describe the interaction between multiple parties working towards a common objective. They also express business rules that govern the behavior of the process and account for expressing changes reflecting new business objectives and new market situations. We developed a service request language and support framework that allow users to formulate their requests against standard business processes.19 In this paper, we extend the approach by presenting a framework capable of automatically associating business rules with relevant processes involved in a user request. This framework plans and monitors the execution of the request and assertions against services underlying these processes. Definitions and classifications of business rules (named assertions in the paper) are given together with an assertion language for expressing them. The framework is able to handle the non-determinism typical for service-oriented computing environments and it is based on the interleaving of planning and execution. Interestingly, the language is able to express both functional and non-functional aspects of the assertions. [ABSTRACT FROM AUTHOR]

Adaptive automated planning systems that can, over time, improve the quality of plans they produce are a promising prospect. The first part of the article discusses the issues involved in designing quality improving learning for planning systems and reviews recent work on learning to improve plan quality. The second part describes our work on the Performance Improving Planning (PIP) System. The heart of PIP is an analytic technique that compares two planning episodes for solving a planning problem that led to two different quality solutions—a higher-quality solution and a lower quality solution—and identifies the critical differences that were responsible for the resulting differences in the quality of the completed plans. We compare the effectiveness of two different ways of storing and applying the knowledge learned from this analysis—as search-control rules and as rewrite rules. The results show that the search-control rules are more effective in improving plan quality. Further analysis of PIP-search-control—the version of PIP that stores the learned knowledge as search-control rules—shows that it is an effective technique for improving plan quality in a variety of situations. [ABSTRACT FROM AUTHOR]

This paper presents an alternative to the 'speech acts with STRIPS' approach to implementing dialogue: a fully implemented AI planner which generates and analyses the semantics of utterances using a single linguistic act for all contexts. Using this act, the planner can model problematic conversational situations, including felicitous and infelicitous instances of bluffing, lying, sarcasm, and stating the obvious. The act has negligible effects, and its precondition can always be proved. 'Speaker maxims' enable the speaker to plan to deceive, as well as to generate implicatures, while 'hearer maxims' enable the hearer to recognise deceptions, and interpret implicatures. The planner proceeds by achieving parts of the constructive proof of a goal. It incorporates an epistemic theorem prover, which embodies a deduction model of belief, and a constructive logic. [ABSTRACT FROM AUTHOR]

AI planning systems tend to be disembodied and are not situated within the environment for which plans are generated, thus losing information concerning the interaction between the system and its environment. This paper argues that such information may potentially be valuable in constraining plan formulation, and presents both an agent- and domain-independent architecture that extends the classical AI planning framework to take into account context, or the interaction between an autonomous situated planning agent and its environment. The paper describes how context constrains the goals an agent might generate, enables those goals to be prioritised, and constrains plan selection. [ABSTRACT FROM AUTHOR]

Constraint satisfaction is a fundamental Artificial Intelligence technique for knowledge representation and inference. However, the formulation of a static constraint satisfaction problem (CSP) with hard, imperative constraints is insufficient to model many real problems. Fuzzy constraint satisfaction provides a more graded viewpoint. Priorities and preferences are placed on individual constraints and aggregated via fuzzy conjunction to obtain a satisfaction degree for a solution to the problem. This paper examines methods for solving an important instance of dynamic flexible constraint satisfaction (DFCSP) combining fuzzy CSP and restriction/relaxation based dynamic CSP: fuzzy rrDFCSP. This allows the modelling of complex situations where both the set of constraints may change over time and there is flexibility inherent in the definition of the problem. This paper also presents a means by which classical planning can be extended via fuzzy sets to enable flexible goals and preferences to be placed on the use of planning operators. A range of plans can be produced, trading compromises made versus the length of the plan. The flexible planning operators are close in definition to fuzzy constraints. Hence, through a hierarchical decomposition of the planning graph, the work shows how flexible planning reduces to the solution of a set of fuzzy rrDFCSPs. [Copyright &y& Elsevier]

This paper summarizes the main existing approaches to propagate resource constraints in Constraint-Based scheduling and identifies some of their limitations for using them in an integrated planning and scheduling framework. We then describe two new algorithms to propagate resource constraints on discrete resources and reservoirs. Unlike most of the classical work in scheduling, our algorithms focus on the precedence relations between activities rather than on their absolute position in time. They are efficient even when the set of activities is not completely defined and when the time window of activities is large. These features explain why our algorithms are particularly suited for integrated planning and scheduling approaches. All our algorithms are illustrated with examples. Encouraging preliminary results are reported on pure scheduling problems as well as some possible extensions of our framework. [Copyright &y& Elsevier]

An automatic design method for power systems to control electric loads required by customers and to ensure the power supply for the loads is described. This method features (I) an electric unit model including the possible connections among the electric units, (II) knowledge association networks to reduce maintenance cost of the knowledge base and to improve efficiency in selecting/specifying electric units, (III) a planning algorithm to find electric paths to control the electric loads and to ensure the power supply, and (IV) the reactive redesign of the automation of editing power systems. APEC3 (Advanced Plant Engineering Cycle tool version 3) is an automatic design tool for power systems that supports electrical engineering processes in developing single line diagrams, specification balance statements (design rationales), and item lists. When engineers modify unit specifications, APEC3 invokes the redesign function which automatically changes the specifications of all relevant units. To evaluate the effectiveness of the method, APEC3 was used in designing practical power systems of steel plants. The results showed that APEC3 can reduce the design time to one-eighth that of the usual design time. Therefore, the engineers can find optimal specifications of the power systems by evaluating a number of possibilities. © 2002 Scripta Technica, Electr Eng Jpn, 139(2): 36–45, 2002; DOI 10.1002/eej.1151 [ABSTRACT FROM AUTHOR]