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.
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Please cite this article in press as: S. Lemaignan et al., Artificial cognition for social Human-Robot interaction: An implementation, Artif. Intell. (2016), http://dx.
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To foster effective use of AI planning and scheduling systems in the real world it is of great importance to both (a) broaden direct access to the technology for the end-users and (b) significantly increase their trust in such technology. Automated Planning and Scheduling (P&S) systems often bring solutions to the users which are neither "obvious" nor immediately acceptable for them. This is because these tools directly reason on causal, temporal and resource constraints; moreover, they employ resolution processes designed to optimize the solution with respect to non trivial evaluation functions. Knowledge-engineering environments aim at simplifying direct access to the technology for people other than the original system designers, while the integration of Validation and Verification (V&V) capabilities in such environments may potentially enhance the users' trust in the technology. Somehow V&V techniques may represent a complementary technology with respect to planning and scheduling, that contribute to develop richer software environments to synthesize a new generation of robust problem-solving applications. The integration of V&V and P&S techniques in a knowledge engineering environment is the topic of this paper. In particular, it analyzes the use of state-of-the-art validation and verification technology to support knowledge engineering for a timeline-based planning system called MrSPOCK. The paper presents the application domain for which the automated solver has been developed, introduces the timeline-based planning ideas and then describes the different possibilities to apply V&V to planning. Hence it continues by describing the step of adding V&V functionalities around the specialized planner MrSPOCK. New functionalities have been added to perform both model validation and plan verification. Lastly, a specific section describes the benefits as well as the performance of such functionalities.
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In recent years research in the planning community has moved increasingly towards application of planners to realistic problems involving both time and many types of resources. For example, interest in planning demonstrated by the space research community has inspired work in observation scheduling, planetary rover exploration and spacecraft control domains. Other temporal and resource-intensive domains including logistics planning , plant control and manufacturing have also helped to focus the community on the modelling and reasoning issues that must be confronted to make planning technology meet the challenges of application. The International Planning Competitions have acted as an important motivating force behind the progress that has been made in planning since 1998. The third competition (held in 2002) set the planning community the challenge of handling time and numeric resources. This necessitated the development of a modelling language capable of expressing temporal and numeric properties of planning domains. In this paper we describe the language, pddl2.1, that was used in the competition. We describe the syntax of the language, its formal semantics and the validation of concurrent plans. We observe that pddl2.1 has considerable modelling power-exceeding the capabilities of current planning technology-and presents a number of important challenges to the research community.
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As we are moving towards the Internet of Things (IoT), the number of sensors deployed around the world is growing at a rapid pace. Market research has shown a significant growth of sensor deployments over the past decade and has predicted a significant increment of the growth rate in the future. These sensors continuously generate enormous amounts of data. However, in order to add value to raw sensor data we need to understand it. Collection, modelling, reasoning, and distribution of context in relation to sensor data plays critical role in this challenge. Context-aware computing has proven to be successful in understanding sensor data. In this paper, we survey context awareness from an IoT perspective. We present the necessary background by introducing the IoT paradigm and context-aware fundamentals at the beginning. Then we provide an in-depth analysis of context life cycle. We evaluate a subset of projects (50) which represent the majority of research and commercial solutions proposed in the field of context-aware computing conducted over the last decade (2001-2011) based on our own taxonomy. Finally, based on our evaluation, we highlight the lessons to be learnt from the past and some possible directions for future research. The survey addresses a broad range of techniques, methods, models, functionalities, systems, applications, and middleware solutions related to context awareness and IoT. Our goal is not only to analyse, compare and consolidate past research work but also to appreciate their findings and discuss their applicability towards the IoT.
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Autonomous robotic systems are complex, hybrid, and often safety-critical; this makes their formal specification and verification uniquely challenging. Though commonly used, testing and simulation alone are insufficient to ensure the correctness of, or provide sufficient evidence for the certification of, autonomous robotics. Formal methods for autonomous robotics has received some attention in the literature, but no resource provides a current overview. This paper systematically surveys the state-of-the-art in formal specification and verification for autonomous robotics. Specially, it identifies and categorises the challenges posed by, the formalisms aimed at, and the formal approaches for the specification and verification of autonomous robotics.
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The goal of this roadmap paper is to summarize the state-of-the-art and identify research challenges when developing, deploying and managing self-adaptive software systems. Instead of dealing with a wide range of topics associated with the field, we focus on four essential topics of self-adaptation: design space for adaptive solutions, processes, from centralized to decentralized control, and practical run-time verification and validation. For each topic, we present an overview, suggest future directions, and focus on selected challenges. This paper complements and extends a previous roadmap on software engineering for self-adaptive systems published in 2009 covering a different set of topics, and reflecting in part on the previous paper. This roadmap is one of the many results of the Dagstuhl Seminar 10431 on Software Engineering for Self-Adaptive Systems, which took place in October 2010.
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This is the fourth workshop on Knowledge and Reasoning in Practical Dialogue Systems. The first workshop was organised at IJCAI-99 in Stockholm, 1 the second workshop took place at IJCAI-2001 in Seattle, 2 and the third workshop was held at IJCAI-2003 in Acapulco. 3 The current workshop includes research in three main areas: dialogue management , adaptive discourse planning, and automatic learning of dialogue policies. Probabilistic and machine learning techniques have significant representation , and the main applications are in robotics and information-providing systems. These workshop notes contain 12 papers that address these issues from various viewpoints. The papers provide stimulating ideas and we believe that they function as a fruitful basis for discussions and further research. The program committee consisted of the colleagues listed below, who were assisted by three additional reviewers. Without the time spent reviewing the submissions and the thoughtful comments provided by these colleagues, the decision process would have been much more difficult. We would like to express our warmest thanks to them all. 1 Selected contributions have been published in a special issue of ETAI, the Electronic Transaction of Artificial Intelligence
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In recent years, formal methods have emerged as an alternative approach to ensuring the quality and correctness of hardware designs, overcoming some of the limitations of traditional validation techniques such as simulation and testing. There are two main aspects to the application of formal methods in a design process: the formal framework used to specify desired properties of a design and the verification techniques and tools used to reason about the relationship between a specification and a corresponding implementation. We survey a variety of frameworks and techniques proposed in the literature and applied to actual designs. The specification frameworks we describe include temporal logics, predicate logic, abstraction and refinement, as well as containment between-regular languages. The verification techniques presented include model checking, automata-theoretic techniques, automated theorem proving, and approaches that integrate the above methods. In order to provide insight into the scope and limitations of currently available techniques, we present a selection of case studies where formal methods were applied to industrial-scale designs, such as microprocessors, floating-point hardware, protocols, memory subsystems, and communications hardware.
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, et al.. Safe and dependable physical human-robot interaction in anthropic domains: State of the art and challenges. Abstract-In the immediate future, metrics related to safety and dependability have to be found in order to successfully introduce robots in everyday environments. The crucial issues needed to tackle the problem of a safe and dependable physical human-robot interaction (pHRI) were addressed in the EURON Perspective Research Project PHRIDOM (Physical Human-Robot Interaction in Anthropic Domains), aimed at charting the new "territory" of pHRI. While there are certainly also "cognitive" issues involved, due to the human perception of the robot (and vice versa), and other objective metrics related to fault detection and isolation, the discussion in this paper will focus on the peculiar aspects of "physical" interaction with robots. In particular, safety and dependability will be the underlying evaluation criteria for mechanical design, actuation, and control architectures. Mechanical and control issues will be discussed with emphasis on techniques that provide safety in an intrinsic way or by means of control components. Attention will be devoted to dependability, mainly related to sensors, control architectures, and fault handling and tolerance. After PHRIDOM, a novel research project has been launched under the Information Society Technologies Sixth Framework Programme of the European Commission. This "Specific Targeted Research or Innovation" project is dedicated to "Physical Human-Robot Interaction: depENDability and Safety" (PHRIENDS). PHRIENDS is about developing key components of the next generation of robots, including industrial robots and assist devices, designed to share the environment and to physically interact with people. The philosophy of the project proposes an integrated approach to the co-design of robots for safe physical interaction with humans, which revolutionizes the classical approach for designing industrial robots-rigid design for accuracy, active control for safety-by creating a new paradigm: design robots that are intrinsically safe, and control them to deliver performance. This paper presents the state of the art in the field as surveyed by the PHRIDOM project, as well as it enlightens a number of challenges that will be undertaken within the PHRIENDS project.
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Cooperative multi-agent planning (MAP) is a relatively recent research field that combines technologies, algorithms and techniques developed by the Artificial Intelligence Planning and Multi-Agent Systems communities. While planning has been generally treated as a single-agent task, MAP generalizes this concept by considering multiple intelligent agents that work cooperatively to develop a course of action that satisfies the goals of the group. This paper reviews the most relevant approaches to MAP, putting the focus on the solvers that took part in the 2015 Competition of Distributed and Multi-Agent Planning, and classifies them according to their key features and relative performance.
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本文描述了一种机器人体系结构,它结合了概率图形模型和声明性编程的互补优势,用不确定性和领域知识的逻辑和概率描述来表示和推理。一种动作语言用于支持非布尔流言和非确定性因果律。该动作语言用于描述两个粒度级别的紧耦合转换图,其中精细分辨率转换图被定义为域的粗分辨率转换图的精细化。粗分辨率系统描述和包括(优先级)默认值的历史记录被转换为答案集Prolog(ASP)程序。对于任何赠品,ASP程序中的推断提供了抽象操作的计划。为了实现每个这样的抽象动作,机器人自动缩放到与该动作相关的精细分辨率过渡图的一部分。然后,在该缩放的精细分辨率系统描述中包括感测和致动中的不确定性的概率表示,并且使用构造部分可观察的马尔可夫决策过程(POMDP)。通过解决POMDP获得的策略被重复调用以将抽象实现实现为一系列具体行为,相应的观察结果记录在粗分辨率历史中并用于后续推理。该架构在模拟和移动机器人中评估室内域中的对象,以表明它支持在复杂域中违反默认,嘈杂观察和不可靠行为的推理。
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Planners developed in the Artificial Intelligence community assume that tasks in the task plans they generate will be executed predictably and reliably. This assumption provides a useful abstraction in that it lets the task planners focus on what tasks should be done, while lower-level motion planners and controllers take care of the details of how the task should be performed. While this assumption is useful in many domains, it becomes problematic when controlling physically embedded systems, where there are often delays, disturbances, and failures. The task plans do not provide enough information about allowed flexibility in task duration and hybrid state evolution. Such flexibility could be useful when deciding how to react to disturbances. An important domain where this gap has caused problems is robotics, particularly, the operation of robots in unstructured, uncertain environments. Due to the complexity of this domain, the demands of tasks to be performed, and the actuation limits of robots, knowledge about permitted flexibility in execution of a task is crucial. We address this gap through two key innovations. First, we specify a Qualitative State Plan (QSP), which supports representation of spatial and temporal flexibility with respect to tasks. Second, we extend compilation approaches developed for temporally flexible execution of discrete activity plans to work with hybrid discrete/continuous systems using a recently developed Linear Quadratic Regulator synthesis algorithm, which performs a state reachability analysis to prune infeasible trajectories, and which determines optimal control policies for feasible state regions. The resulting Model-based Executive is able to take advantage of spatial and temporal flexibility in a QSP to improve handling of disturbances. Note that in this work, we focus on execution of QSPs, and defer the problem of how they are generated. We believe the latter could be accomplished through extensions to existing task planners.
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In this paper, the recent results of the space project IMPERA are presented. The goal of IMPERA is the development of a multirobot planning and plan execution architecture with a focus on a lunar sample collection scenario in an unknown environment. We describe the implementation and verification of different modules that are integrated into a distributed system architecture. The modules include a mission planning approach for a multirobot system and modules for task and skill execution within a lunar use-case scenario. The skills needed for the test scenario include cooperative exploration and mapping strategies for an unknown environment, the localization and classification of sample containers using a novel approach of semantic perception, and the skill of transporting sample containers to a collection point using a mobile manipulation robot. Additionally, we present our approach of a reliable communication framework that can deal with communication loss during the mission. Several modules are tested within several experiments in the domain of planning and plan execution, communication, coordinated exploration, perception, and object transportation. An overall system integration is tested on a mission scenario experiment using three robots. C 2013 Wiley Periodicals, Inc.
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新一代分布式系统为逻辑编程(LP)开辟了新的视角:一方面,面向服务的架构代表了当今分布式系统工程的标准方法;另一方面,普适系统要求位置智能。在本文中,我们将逻辑编程即服务(LPaaS)概念介绍为通过逻辑引擎作为分布式服务来满足普适智能系统的需求。首先,我们通过在新的上下文中重新解释经典的LP概念来定义抽象体系结构模型;然后我们通过描述基本的LPaaS接口来详细阐述LP被解释为服务的本质。最后,我们通过讨论分布式tuProlog引擎的实现,解决LPaaS在实践中的工作原理,解决了互操作性和可配置性等基本问题。
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Planning under uncertainty is a central problem in the study of automated sequential decision making, and has been addressed by researchers in many diierent elds, including AI planning, decision analysis, operations research, control theory and economics. While the assumptions and perspectives adopted in these areas often diier in substantial ways, many planning problems of interest to researchers in these elds can be modeled as Markov decision processes (MDPs) and analyzed using the techniques of decision theory. This paper presents an overview and synthesis of MDP-related methods, showing how they provide a unifying framework for modeling many classes of planning problems studied in AI. It also describes structural properties of MDPs that, when exhibited by particular classes of problems, can be exploited in the construction of optimal or approximately optimal policies or plans. Planning problems commonly possess structure in the reward and value functions used to describe performance criteria, in the functions used to describe state transitions and observations, and in the relationships among features used to describe states, actions, rewards, and observations. Specialized representations, and algorithms employing these representations, can achieve computational leverage by exploiting these various forms of structure. Certain AI techniques| in particular those based on the use of structured, intensional representations|can be viewed in this way. This paper surveys several types of representations for both classical and decision-theoretic planning problems, and planning algorithms that exploit these representations in a number of diierent ways to ease the computational burden of constructing policies or plans. It focuses primarily on abstraction, aggregation and decomposition techniques based on AI-style representations.
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