Universal Properties of Pseudoscalar Mediators

We discuss universal signals of consistent models of pseudoscalar mediators for collider searches for Dark Matter. Keeping only the degrees of freedom that can not be decoupled due to consistency conditions, we present a universality class of simplified models with pseudoscalar mediators and renormalizable couplings to Standard Model fields. We compute stability and perturbativity constraints, constraints from electroweak precision measurements, collider searches for new heavy particles as well as constraints from relic density measurements and indirect detection experiments searching for signals of Dark Matter annihilation into photons. We find that the mono-$Z$ final state is the strongest, universal signal of this class of models, with additional signatures present in the different ultraviolet completions that can be used to distinguish between them

An ontology for failure interpretation in automated planning and execution

This is a post-peer-review, pre-copyedit version of an article published in ROBOT - Iberian Robotics Conference. The final authenticated version is available online at: http://dx.doi.org/10.1007/978-3-030-35990-4_31”.Autonomous indoor robots are supposed to accomplish tasks, like serve a cup, which involve manipulation actions, where task and motion planning levels are coupled. In both planning levels and execution phase, several source of failures can occur. In this paper, an interpretation ontology covering several sources of failures in automated planning and also during the execution phases is introduced with the purpose of working the planning more informed and the execution prepared for recovery. The proposed failure interpretation ontological module covers: (1) geometric failures, that may appear when e.g. the robot can not reach to grasp/place an object, there is no free-collision path or there is no feasible Inverse Kinematic (IK) solution. (2) hardware related failures that may appear when e.g. the robot in a real environment requires to be re-calibrated (gripper or arm), or it is sent to a non-reachable configuration. (3) software agent related failures, that may appear when e.g. the robot has software components that fail like when an algorithm is not able to extract the proper features. The paper describes the concepts and the implementation of failure interpretation ontology in several foundations like DUL and SUMO, and presents an example showing different situations in planning demonstrating the range of information the framework can provide for autonomous robotsPeer ReviewedPostprint (author's final draft

Assembly planning in cluttered environments through heterogeneous reasoning

Assembly recipes can elegantly be represented in description logic theories. With such a recipe, the robot can figure out the next assembly step through logical inference. However, before performing an action, the robot needs to ensure various spatial constraints are met, such as that the parts to be put together are reachable, non occluded, etc. Such inferences are very complicated to support in logic theories, but specialized algorithms exist that efficiently compute qualitative spatial relations such as whether an object is reachable. In this work, we combine a logic-based planner for assembly tasks with geometric reasoning capabilities to enable robots to perform their tasks under spatial constraints. The geometric reasoner is integrated into the logic-based reasoning through decision procedures attached to symbols in the ontology.Peer ReviewedPostprint (author's final draft

RoboEarth Semantic Mapping: A Cloud Enabled Knowledge-Based Approach

The vision of the RoboEarth project is to design a knowledge-based system to provide web and cloud services that can transform a simple robot into an intelligent one. In this work, we describe the RoboEarth semantic mapping system. The semantic map is composed of: 1) an ontology to code the concepts and relations in maps and objects and 2) a SLAM map providing the scene geometry and the object locations with respect to the robot. We propose to ground the terminological knowledge in the robot perceptions by means of the SLAM map of objects. RoboEarth boosts mapping by providing: 1) a subdatabase of object models relevant for the task at hand, obtained by semantic reasoning, which improves recognition by reducing computation and the false positive rate; 2) the sharing of semantic maps between robots; and 3) software as a service to externalize in the cloud the more intensive mapping computations, while meeting the mandatory hard real time constraints of the robot. To demonstrate the RoboEarth cloud mapping system, we investigate two action recipes that embody semantic map building in a simple mobile robot. The first recipe enables semantic map building for a novel environment while exploiting available prior information about the environment. The second recipe searches for a novel object, with the efficiency boosted thanks to the reasoning on a semantically annotated map. Our experimental results demonstrate that, by using RoboEarth cloud services, a simple robot can reliably and efficiently build the semantic maps needed to perform its quotidian tasks. In addition, we show the synergetic relation of the SLAM map of objects that grounds the terminological knowledge coded in the ontology

Integrating Know-How into the Linked Data Cloud

This paper presents the first framework for integrating procedural knowledge, or "know-how", into the Linked Data Cloud. Know-how available on the Web, such as step-by-step instructions, is largely unstructured and isolated from other sources of online knowledge. To overcome these limitations, we propose extending to procedural knowledge the benefits that Linked Data has already brought to representing, retrieving and reusing declarative knowledge. We describe a framework for representing generic know-how as Linked Data and for automatically acquiring this representation from existing resources on the Web. This system also allows the automatic generation of links between different know-how resources, and between those resources and other online knowledge bases, such as DBpedia. We discuss the results of applying this framework to a real-world scenario and we show how it outperforms existing manual community-driven integration efforts.Comment: The 19th International Conference on Knowledge Engineering and Knowledge Management (EKAW 2014), 24-28 November 2014, Link\"oping, Swede

Automated Planning Techniques for Robot Manipulation Tasks Involving Articulated Objects

The goal-oriented manipulation of articulated objects plays an important role in real-world robot tasks. Current approaches typically pose a number of simplifying assumptions to reason upon how to obtain an articulated object’s goal configuration, and exploit ad hoc algorithms. The consequence is two-fold: firstly, it is difficult to generalise obtained solutions (in terms of actions a robot can execute) to different target object’s configurations and, in a broad sense, to different object’s physical characteristics; secondly, the representation and the reasoning layers are tightly coupled and inter-dependent. In this paper we investigate the use of automated planning techniques for dealing with articulated objects manipulation tasks. Such techniques allow for a clear separation between knowledge and reasoning, as advocated in Knowledge Engineering. We introduce two PDDL formulations of the task, which rely on conceptually different representations of the orientation of the objects. Experiments involving several planners and increasing size objects demonstrate the effectiveness of the proposed models, and confirm its exploitability when embedded in a real-world robot software architecture

Role and task allocation framework for Multi-Robot Collaboration with latent knowledge estimation

In this work a novel framework for modeling role and task allocation in Cooperative Heterogeneous Multi-Robot Systems (CHMRSs) is presented. This framework encodes a CHMRS as a set of multidimensional relational structures (MDRSs). This set of structure defines collaborative tasks through both temporal and spatial relations between processes of heterogeneous robots. These relations are enriched with tensors which allow for geometrical reasoning about collaborative tasks. A learning schema is also proposed in order to derive the components of each MDRS. According to this schema, the components are learnt from data reporting the situated history of the processes executed by the team of robots. Data are organized as a multirobot collaboration treebank (MRCT) in order to support learning. Moreover, a generative approach, based on a probabilistic model, is combined together with nonnegative tensor decomposition (NTD) for both building the tensors and estimating latent knowledge. Preliminary evaluation of the performance of this framework is performed in simulation with three heterogeneous robots, namely, two Unmanned Ground Vehicles (UGVs) and one Unmanned Aerial Vehicle (UAV)

Non-affirmative Theory of Education as a Foundation for Curriculum Studies, Didaktik and Educational Leadership

This chapter presents non-affirmative theory of education as the foundation for a new research program in education, allowing us to bridge educational leadership, curriculum studies and Didaktik. We demonstrate the strengths of this framework by analyzing literature from educational leadership and curriculum theory/didaktik. In contrast to both socialization-oriented explanations locating curriculum and leadership within existing society, and transformation-oriented models viewing education as revolutionary or super-ordinate to society, non-affirmative theory explains the relation between education and politics, economy and culture, respectively, as non-hierarchical. Here critical deliberation and discursive practices mediate between politics, culture, economy and education, driven by individual agency in historically developed cultural and societal institutions. While transformative and socialization models typically result in instrumental notions of leadership and teaching, non-affirmative education theory, previously developed within German and Nordic education, instead views leadership and teaching as relational and hermeneutic, drawing on ontological core concepts of modern education: recognition; summoning to self-activity and Bildsamkeit. Understanding educational leadership, school development and teaching then requires a comparative multi-level approach informed by discursive institutionalism and organization theory, in addition to theorizing leadership and teaching as cultural-historical and critical-hermeneutic activity. Globalisation and contemporary challenges to deliberative democracy also call for rethinking modern nation-state based theorizing of education in a cosmopolitan light. Non-affirmative education theory allows us to understand and promote recognition based democratic citizenship (political, economical and cultural) that respects cultural, ethical and epistemological variations in a globopolitan era. We hope an American-European-Asian comparative dialogue is enhanced by theorizing education with a non-affirmative approach