Dynamic Branching in Qualitative Constraint Networks via Counting Local Models

We introduce and evaluate dynamic branching strategies for solving Qualitative Constraint Networks (QCNs), which are networks that are mostly used to represent and reason about spatial and temporal information via the use of simple qualitative relations, e.g., a constraint can be "Task A is scheduled after or during Task C". In qualitative constraint-based reasoning, the state-of-the-art approach to tackle a given QCN consists in employing a backtracking algorithm, where the branching decisions during search are governed by the restrictiveness of the possible relations for a given constraint (e.g., after can be more restrictive than during). In the literature, that restrictiveness is defined a priori by means of static weights that are precomputed and associated with the relations of a given calculus, without any regard to the particulars of a given network instance of that calculus, such as its structure. In this paper, we address this limitation by proposing heuristics that dynamically associate a weight with a relation, based on the count of local models (or local scenarios) that the relation is involved with in a given QCN; these models are local in that they focus on triples of variables instead of the entire QCN. Therefore, our approach is adaptive and seeks to make branching decisions that preserve most of the solutions by determining what proportion of local solutions agree with that decision. Experimental results with a random and a structured dataset of QCNs of Interval Algebra show that it is possible to achieve up to 5 times better performance for structured instances, whilst maintaining non-negligible gains of around 20% for random ones

SparQ-A Spatial Reasoning Toolbox

Abstract SparQ is a toolbox for qualitative spatial reasoning. Interpreting reasoning in a broad sense, SparQ covers mapping information from quantitative to qualitative, applying constraint reasoning to qualitative information, reasoning about calculi, and mapping qualitative information back to the quantitative domain. The toolbox is designed for extensibility and released under the GNU GPL public license for free software

Qualitative Arrangement Information for Matching

In the context of a generalized robot localization task we investigate the utility of qualitative arrangement information in recognition tasks. Qualitative information allows us to make certain knowledge explicit, separating it from uncertain information that we are facing in recognition tasks. This can give rise to efficient matching algorithms for recognition tasks. Particularly qualitative ordering information is very helpful: it can adequately capture certain spatial knowledge and leads to efficient polynomial-time matching algorithms

Spatial Information Extraction from Text Using Spatio-Ontological Reasoning (Short Paper)

This paper is involved with extracting spatial information from text. We seek to geo-reference all spatial entities mentioned in a piece of text. The focus of this paper is to investigate the contribution of spatial and ontological reasoning to spatial interpretation of text. A preliminary study considering descriptions of cities and geographical regions from English Wikipedia suggests that spatial and ontological reasoning can be more effective to resolve ambiguities in text than a classical text understanding pipeline relying on parsing

Leveraging Qualitative Reasoning to Learning Manipulation Tasks

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