Spatial Logic + Temporal Logic = ?

Kontchakov, Roman; Kurucz, Agi; Wolter, Frank; Zakharyaschev, Michael

doi:10.1007/978-1-4020-5587-4_9

Cited by 52 publications

(40 citation statements)

References 63 publications

(64 reference statements)

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“…For the spatial reasoning we focus on topological spatial modeling and inference. Combining spatial and temporal logics is not something new- Kontchakov et al (2007) provide a lengthy overview of different techniques that have been employed to combine the two. However, the application of these techniques to the domain of stream reasoning requires a different approach, which is what is presented in this thesis.…”

Section: Spatio-temporal Stream Reasoningmentioning

confidence: 99%

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Spatio-Temporal Stream Reasoning with Adaptive State Stream Generation

Leng¹

2017

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A lot of today's data is generated incrementally over time by a large variety of producers. This data ranges from quantitative sensor observations produced by robot systems to complex unstructured human-generated texts on social media. With data being so abundant, making sense of these streams of data through reasoning is challenging. Reasoning over streams is particularly relevant for autonomous robotic systems that operate in a physical environment. They commonly observe this environment through incremental observations, gradually refining information about their surroundings. This makes robust management of streaming data and its refinement an important problem. Many contemporary approaches to stream reasoning focus on the issue of querying data streams in order to generate higher-level information by relying on wellknown database approaches. Other approaches apply logic-based reasoning techniques, which rarely consider the provenance of their symbolic interpretations. In this thesis, we integrate techniques for logic-based spatio-temporal stream reasoning with the adaptive generation of the state streams needed to do the reasoning over. This combination deals with both the challenge of reasoning over streaming data and the problem of robustly managing streaming data and its refinement. The main contributions of this thesis are (1) a logic-based spatio-temporal reasoning technique that combines temporal reasoning with qualitative spatial reasoning; (2) an adaptive reconfiguration procedure for generating and maintaining a data stream required to perform spatio-temporal stream reasoning over; and (3) integration of these two techniques into a stream reasoning framework. The proposed spatio-temporal stream reasoning technique is able to reason with intertemporal spatial relations by leveraging landmarks. Adaptive state stream generation allows the framework to adapt in situations in which the set of available streaming resources changes. Management of streaming resources is formalised in the DyKnow model, which introduces a configuration life-cycle to adaptively generate state streams. The DyKnow-ROS stream reasoning framework is a concrete realisation of this model that extends the Robot Operating System (ROS). DyKnow-ROS has been deployed on the SoftBank Robotics NAO platform to demonstrate the system's capabilities in the context of a case study on run-time adaptive reconfiguration. The results show that the proposed system-by combining reasoning over and reasoning about streams-can robustly perform spatio-temporal stream reasoning, even when the availability of streaming resources changes.

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Section: Spatio-temporal Stream Reasoningmentioning

confidence: 99%

“…A survey of other approaches that combine spatial and temporal reasoning techniques is provided by Kontchakov et al (2007).…”

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confidence: 99%

Spatio-Temporal Stream Reasoning with Adaptive State Stream Generation

Leng¹

2017

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“…For example, we dare to represent a learning task declaratively by saying that a start configuration (e.g., can on desk) should be changed to obtain some desired end configuration (e.g., can in dustbin). Sequences of such statements are also called snapshot-based spatio-temporal models [11]. In its generality, the question of combining a qualitative spatial representation with a temporal representation is tackled in the contemporary field of spatial logics [11] which comprises several demanding research questions.…”

Section: Qualitative Representation Of Manipulation Tasks With Qualitmentioning

confidence: 99%

“…Sequences of such statements are also called snapshot-based spatio-temporal models [11]. In its generality, the question of combining a qualitative spatial representation with a temporal representation is tackled in the contemporary field of spatial logics [11] which comprises several demanding research questions. As our approach does not depend on specific reasoning properties, we opt for a straightforward combination of linear temporal logic (LTL) with a qualitative spatial representation.…”

Section: Qualitative Representation Of Manipulation Tasks With Qualitmentioning

confidence: 99%

Leveraging Qualitative Reasoning to Learning Manipulation Tasks

Wolter

Kirsch

2015

Robotics

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Learning and planning are powerful AI methods that exhibit complementary strengths. While planning allows goal-directed actions to be computed when a reliable forward model is known, learning allows such models to be obtained autonomously. In this paper we describe how both methods can be combined using an expressive qualitative knowledge representation. We argue that the crucial step in this integration is to employ a representation based on a well-defined semantics. This article proposes the qualitative spatial logic QSL, a representation that combines qualitative abstraction with linear temporal logic, allowing us to represent relevant information about the learning task, possible actions, and their consequences. Doing so, we empower reasoning processes to enhance learning performance beyond the positive effects of learning in abstract state spaces. Proof-of-concept experiments in two simulation environments show that this approach can help to improve learning-based robotics by quicker convergence and leads to more reliable action planning.Keywords: qualitative spatial reasoning; robot learning; AI robotics MotivationRobotic applications have evolved considerably, yet they still lack the efficiency, flexibility and adaptability that is needed to improve our everyday life as versatile service robots. Planning and learning Robotics 2015, 4 254 are the two major paradigms employed to make a robot act intelligently. Planning is a form of symbolic reasoning applied on various levels of abstraction. Planning is effective whenever reliable forward models of the robot and its environment are available. Learning is a powerful method to generate such models from data. However, it requires carefully handcrafted state space representations to converge to high-quality models in reasonable time. Neither planning nor learning on their own suffice to meet the demands of versatile service robots. By integrating both paradigms one can benefit from their respective strengths. So far, a true integration of learning and planning has not been achieved that allows a robot to acquire new skills efficiently in a truly autonomous way.Our work is motivated by this basic question of how to integrate learning and planning, leveraging their individual strengths to produce reliable and versatile robot behavior. We propose a qualitative reasoning framework as the glue between those two paradigms. Qualitative reasoning lifts information, e.g., obtained by perception, to a knowledge level, enabling us to integrate it with common sense knowledge and to use available reasoning techniques [1][2][3]. The qualitative representation serves two purposes: (1) to make a planning or action selection process with learned models more efficient; and (2) to control selection of learning samples in order to make a learning process more reliable and to obtain high-quality models with little manual design effort. We present two experimental studies in simulation for a specific robot task: to throw an object to a specific destination. The results ind...

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“…Proving preservation results constitute a theoretical challenge and, in many cases, they are obtained only by imposing conditions on the original logics. It is interesting to observe that combination mechanisms start to play an essential role in contemporary applications, like argumentation theory, spatial and temporal reasoning and information security [14,6,15]. In some of these topics, the logical context has to be set-up appropriately.…”

Section: Introductionmentioning

confidence: 99%

On Graph-theoretic Fibring of Logics

Sernadas¹,

Sernadas²,

Rasga³

et al. 2009

Journal of Logic and Computation

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A graph-theoretic account of fibring of logics is developed, capitalizing on the interleaving characteristics of fibring at the linguistic, semantic and proof levels. Fibring of two signatures is seen as an m-graph where the nodes and the m-edges include the sorts and the constructors of the signatures at hand. Fibring of two models is an m-graph where the nodes and the m-edges are the values and the operations in the models, respectively. Fibring of two deductive systems is an m-graph whose nodes are language expressions and the m-edges represent the inference rules of the two original systems. The sobriety of the approach is confirmed by proving that all the fibring notions are universal constructions. This graph-theoretic view is general enough to accommodate very different fibrings of propositional based logics encompassing logics with non-deterministic semantics, logics with an algebraic semantics, logics with partial semantics, and substructural logics, among others. Soundness and weak completeness are proved to be preserved under very general conditions. Strong completeness is also shown to be preserved under tighter conditions. In this setting, the collapsing problem appearing in several combinations of logic systems can be avoided.

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Spatial Logic + Temporal Logic = ?

Cited by 52 publications

References 63 publications

Spatio-Temporal Stream Reasoning with Adaptive State Stream Generation

Spatio-Temporal Stream Reasoning with Adaptive State Stream Generation

Leveraging Qualitative Reasoning to Learning Manipulation Tasks

On Graph-theoretic Fibring of Logics

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