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We present new planning and learning algorithms for RAE, the Refinement Acting Engine. RAE uses hierarchical operational models to perform tasks in dynamically changing environments. Our planning procedure, UPOM, does a UCT-like search in the space of operational models in order to find a near-optimal method to use for the task and context at hand. Our learning strategies acquire, from online acting experiences and/or simulated planning results, a mapping from decision contexts to method instances as well as a heuristic function to guide UPOM. Our experimental results show that UPOM and our learning strategies significantly improve RAE's performance in four test domains using two different metrics: efficiency and success ratio., Accepted in ICAPS 2020 (30th International Conference on Automated Planning and Scheduling)

Most of the works on planning and learning, e.g., planning by (model based) reinforcement learning, are based on two main assumptions: (i) the set of states of the planning domain is fixed; (ii) the mapping between the observations from the real word and the states is implicitly assumed or learned offline, and it is not part of the planning domain. Consequently, the focus is on learning the transitions between states. In this paper, we drop such assumptions. We provide a formal framework in which (i) the agent can learn dynamically new states of the planning domain; (ii) the mapping between abstract states and the perception from the real world, represented by continuous variables, is part of the planning domain; (iii) such mapping is learned and updated along the "life" of the agent. We define an algorithm that interleaves planning, acting, and learning, and allows the agent to update the planning domain depending on how much it trusts the model w.r.t. the new experiences learned by executing actions. We define a measure of coherence between the planning domain and the real world as perceived by the agent. We test our approach showing that the agent learns increasingly coherent models, and that the system can scale to deal with models with an order of $10^6$ states.

International audience; We give a formalism and an algorithm for synthesizing controllers to coordinate interactions among hierarchically organized agents. Typical applications are, for example, in harbor or warehouse automation. The formalism models agents as hierarchical input/output automata, and models a system of interacting agents as the parallel composition of the automata. It extends the usual parallel composition operation of I/O automata with a hierarchical composition operation for refining abstract tasks into lower-level subtasks. We provide an algorithm to synthesize hierarchically organized controllers to coordinate the agents' interactions in order to drive the system toward desired states. Our contribution is mostly theoretical: we formally define the representation, and present theorems about its properties (i.e., the parallel and hierarchical composition are distributive operations), as well as the correctness and completeness of the synthesis algorithm.

International audience; A significant problem for integrating acting and planning is how to maintain consistency between the planner's descriptive action models, which abstractly describe what the actions do, and the actor's operational models, which tell how to perform the actions with rich control structures for closed-loop online decision-making. Operational models allow for dealing with a variety of contexts, and responding to unexpected outcomes and events in a dynamically changing environment. To circumvent the consistency problem, we use the actor's operational models both for acting and for planning. Our acting-and-planning algorithm, APE, uses hierarchical operational models inspired from those in the well-known PRS system. But unlike the reactive PRS algorithm, APE chooses its course of action using a planner that does Monte Carlo sampling over simulated executions of APE's operational models. Our experiments with this approach show significant benefits in the success rates of the acting system, in particular for domains with dead ends.

International audience; This paper introduces an original framework for planning and acting with hierarchical input/output automata for systems defined by the parallel composition of the models of their components. Typical applications are, for example, in harbor or warehouse automation. The framework extends the usual parallel composition operation of I/O au-tomata with a hierarchical composition operation that can refine a task. It defines planning as the synthesis of a control component to drive, through I/O interactions and task refinement, the system toward desired states. A new nondeterministic algorithm performs this synthesis. We tackle these issues on a theoretical basis. We formally define the representation and prove that the two operations of parallel and hierarchical composition are distributive, which is essential for the correctness and completeness of the proposed planning algorithm.

By understanding the tremendous opportunities to work with social media data and the acknowledgment of the negative effects social media messages can have, a way of assessing truth in claims on social media would not only be interesting but also very valuable. By making use of this ability, applications using social media data could be supported, or a selection tool in research regarding the spread of false rumors or 'fake news' could be build. In this paper, we show that we can determine truth by using a statistical classifier supported by an architecture of three preprocessing phases. We base our research on a dataset of Twitter messages about the FIFA World Cup 2014. We determine the truth of a tweet by using 7 popular fact types (involving events in the matches in the tournament such as scoring a goal) and we show that we can achieve an F1-score of 0.988 for the first class; the Tweets which contain no false facts and an F1-score of 0.818 on the second class; the Tweets which contain one or more false facts.

Online consumer reviews, product and services recommendations and peer opinions play an increasingly growing role in the customer’s decision making process. The various online product review and recommendation platforms differ in their objectives, function and characteristics. The literature has so far paid little attention on function characteristics of these platforms as an element of customer adoption and preference. Given the importance of this form of customer generated content on business sales and profitability the monitoring and often responding to customer reviews by business organizations has become a major managerial challenge and an important reputation management issue. In order to respond efficiently to customer reviews companies need to identify consumer reviews platforms, understand their characteristics and continuously assess their impact on consumer purchasing decisions. This study identifies four main types of online review platforms: retail websites, independent reviewing platforms, video-sharing platforms and personal blogs. These platforms present product reviews in different formats with accent on various review function characteristics. An online survey analyzed consumer opinions about the various platforms and review mechanisms and the impact of those on consumer buying behavior. The results underline the importance of platform credibility and usability on consumer trust and reliance in reviews as input in the decision-making process

In this paper we argue why it is necessary to associate linguistic information with ontologies and why more expressive models, beyond RDFS, OWL and SKOS, are needed to capture the relation between natural language constructs on the one hand and ontological entities on the other. We argue that in the light of tasks such as ontology-based information extraction, ontology learning and population from text and natural language generation from ontologies, currently available datamodels are not sufficient as they only allow to associate atomic terms without linguistic grounding or structure to ontology elements. Towards realizing a more expressive model for associating linguistic information to ontology elements, we base our work presented here on previously developed models (LingInfo, LexOnto, LMF ) and present a new joint model for linguistic grounding of ontologies called LexInfo . LexInfo combines essential design aspects of LingInfo and LexOnto and builds on a sound model for representing computational lexica called LMF which has been recently approved as a standard under ISO.

From 4.2.2007 to 9.2.2007, the Dagstuhl Seminar 07061 ``Autonomous and Adaptive Web Services'' was held in the International Conference and Research Center (IBFI), Schloss Dagstuhl. During the seminar, several participants presented their current research, and ongoing work and open problems were discussed. Abstracts of the presentations given during the seminar as well as abstracts of seminar results and ideas are put together in this paper. The first section describes the seminar topics and goals in general. Links to extended abstracts or full papers are provided, if available.

This document presents a Services Research Roadmap that launches four pivotal, inherently related, research themes to Service-Oriented Computing (SOC): service foundations, service composition, service management and monitoring and service-oriented engineering. Each theme is introduced briefly from a technology, state of the art and scientific challenges standpoint. From the technology standpoint a comprehensive review of state of the art, standards, and current research activities in each key area is provided. From the state of the art the major open problems and bottlenecks to progress are identified. During the during seminar each core theme was initially introduced by a leading expert in the field who described the state of the art and highlighting open problems and important research topics for the SOC community to work on in the future. These experts were then asked to coordinate parallel workgroups that were entrusted with an in-depth analysis of the research opportunities and needs in the respective theme. The findings presented in this summary report build on the advice of those panels of experts from industry and academia who participated in this Dagstuhl Seminar and met at other occasions during the past three years, e.g., at the International Conference on Service Oriented Computing (ICSOC, see www.icsoc.org). These experts represent many disciplines including distributed computing, database and information systems, software engineering, computer architectures and middleware and knowledge representation.