A Model-Driven Engineering Approach for ROS using Ontological Semantics

This paper presents a novel ontology-driven software engineering approach for the development of industrial robotics control software. It introduces the ReApp architecture that synthesizes model-driven engineering with semantic technologies to facilitate the development and reuse of ROS-based components and applications. In ReApp, we show how different ontological classification systems for hardware, software, and capabilities help developers in discovering suitable software components for their tasks and in applying them correctly. The proposed model-driven tooling enables developers to work at higher abstraction levels and fosters automatic code generation. It is underpinned by ontologies to minimize discontinuities in the development workflow, with an integrated development environment presenting a seamless interface to the user. First results show the viability and synergy of the selected approach when searching for or developing software with reuse in mind.Comment: Presented at DSLRob 2015 (arXiv:1601.00877), Stefan Zander, Georg Heppner, Georg Neugschwandtner, Ramez Awad, Marc Essinger and Nadia Ahmed: A Model-Driven Engineering Approach for ROS using Ontological Semantic

Agent-based semantic composition of Web services using distributed description logics

International audienceAn important research challenge consists in composing web services in an automatic and distributed manner on a large scale. Indeed, most queries can not be satisfiable by one service and must be processed by composing several services. Each web service is often written by different designers and is described using the terms of their own ontology. Therefore, the composition process needs to deal with a variety of heterogeneous ontologies. In order to tackle this challenge, we propose an approach using Distributed Description Logics (DDL) to achieve the semantic composition of web services. DDL allows one to make semantic connections between ontologies and thus web services, as well as to reason to get a semantic composition of web services

Towards Ontology-Mediated Planning with OWL DL Ontologies (Extended Version)

While classical planning languages make the closed-domain and closed-world assumption, there have been various approaches to extend those with DL reasoning, which is then interpreted under the usual open-world semantics. Current approaches for planning with DL ontologies integrate the DL directly into the planning language, and practical approaches have been developed based on first-order rewritings or rewritings into datalog. We present here a new approach in which the planning specification and ontology are kept separate, and are linked together using an interface. This allows planning experts to work in a familiar formalism, while existing ontologies can be easily integrated and extended by ontology experts. Our approach for planning with those ontology-mediated planning problems is optimized for cases with comparatively small domains, and supports the whole OWL DL fragment. The idea is to rewrite the ontology-mediated planning problem into a classical planning problem to be processed by existing planning tools. Different to other approaches, our rewriting is data-dependent. A first experimental evaluation of our approach shows the potential and limitations of this approach.Comment: Extended version of a paper accepted at 36th International Workshop on Description Logics (DL 2023

A Categorical Representation Language and Computational System for Knowledge-Based Planning

Classical planning representation languages based on first-order logic have been extensively used to model and solve planning problems, but they struggle to capture implicit preconditions and effects that arise in complex planning scenarios. To address this problem, we propose an alternative approach to representing and transforming world states during planning. Based on the category-theoretic concepts of $\mathsf{C}$-sets and double-pushout rewriting (DPO), our proposed representation can effectively handle structured knowledge about world states that support domain abstractions at all levels. It formalizes the semantics of predicates according to a user-provided ontology and preserves the semantics when transitioning between world states. This method provides a formal semantics for using knowledge graphs and relational databases to model world states and updates in planning. In this paper, we compare our category-theoretic representation with the classical planning representation. We show that our proposed representation has advantages over the classical representation in terms of handling implicit preconditions and effects, and provides a more structured framework in which to model and solve planning problems

APE in the wild: automated exploration of proteomics workflows in the bio.tools registry

The bio.tools registry is a main catalogue of computational tools in the life sciences. More than 17 000 tools have been registered by the international bioinformatics community. The bio.tools metadata schema includes semantic annotations of tool functions, that is, formal descriptions of tools' data types, formats, and operations with terms from the EDAM bioinformatics ontology. Such annotations enable the automated composition of tools into multistep pipelines or workflows. In this Technical Note, we revisit a previous case study on the automated composition of proteomics workflows. We use the same four workflow scenarios but instead of using a small set of tools with carefully handcrafted annotations, we explore workflows directly on bio.tools. We use the Automated Pipeline Explorer (APE), a reimplementation and extension of the workflow composition method previously used. Moving "into the wild" opens up an unprecedented wealth of tools and a huge number of alternative workflows. Automated composition tools can be used to explore this space of possibilities systematically. Inevitably, the mixed quality of semantic annotations in bio.tools leads to unintended or erroneous tool combinations. However, our results also show that additional control mechanisms (tool filters, configuration options, and workflow constraints) can effectively guide the exploration toward smaller sets of more meaningful workflows.Proteomic

A review and comparison of ontology-based approaches to robot autonomy

Within the next decades, robots will need to be able to execute a large variety of tasks autonomously in a large variety of environments. To relax the resulting programming effort, a knowledge-enabled approach to robot programming can be adopted to organize information in re-usable knowledge pieces. However, for the ease of reuse, there needs to be an agreement on the meaning of terms. A common approach is to represent these terms using ontology languages that conceptualize the respective domain. In this work, we will review projects that use ontologies to support robot autonomy. We will systematically search for projects that fulfill a set of inclusion criteria and compare them with each other with respect to the scope of their ontology, what types of cognitive capabilities are supported by the use of ontologies, and which is their application domain.Peer ReviewedPostprint (author's final draft

Housekeeping with multiple autonomous robots: representation, reasoning, and execution

We consider a housekeeping domain with static or movable objects, where the goal is for multiple autonomous robots to tidy a house collaboratively in a given amount of time. This domain is challenging in the following ways: commonsense knowledge (e.g., expected locations of objects in the house) is required for intelligent behavior of robots; geometric constraints are required to find feasible plans (e.g., to avoid collisions); in case of plan failure while execution (e.g., due to a collision with movable objects whose presence and location are not known in advance or due to heavy objects that cannot be lifted by a single robot), recovery is required depending on the cause of failure; and collaboration of robots is required to complete some tasks (e.g., carrying heavy objects). We introduce a formal planning, execution and monitoring framework to address the challenges of this domain, by embedding knowledge representation and automated reasoning in each level of decision-making (that consists of discrete task planning, continuous motion planning, and plan execution), in such a way as to tightly integrate these levels. At the high-level, we represent not only actions and change but also commonsense knowledge in a logicbased formalism. Geometric reasoning is lifted to the high-level by embedding motion planning in the domain description. Then a discrete plan is computed for each robot using an automated reasoner. At the mid-level, if a continuous trajectory cannot be computed by a motion planner because the discrete plan is not feasible at the continuous-level, then a different plan is computed by the automated reasoner subject to some (temporal) conditions represented as formulas. At the low-level, if the plan execution fails, then a new continuous trajectory is computed by a motion planner at the mid-level or a new discrete plan is computed using an automated reasoner at the high-level. We illustrate the applicability of this formal framework with a simulation of a housekeeping domain