Array Representations for Model-Based Spatial Reasoning

To date, the major focus of research in knowledge representations for artificial intelligence has been on sentential or linguistic formalisms involving logic and rulebased reasoning. There is a growing body of evidence suggesting, however, that much of human problem solving is achieved, not through the application of rules of inference, but rather through the manipulation of mental models. Such a model is represented by a system with a similar relational structure to the reality it represents. Moreover, spatial reasoning with models involves the inspection and transformation of representations in ways that are analogous to visually inspecting and physically transforming entities in the world. Since a crucial component of knowledge acquisition is to capture an expert's mental state and reasoning strategies, it is important to shift some of the attention of AI research to the study of representation techniques that correspond to the mental models used by humans. The paper begins with a cognitive perspective on model-based reasoning. A knowledge representation scheme for spatial reasoning with models is then presented. In this scheme, which has evolved from research in computational imagery, spatial models are represented as symbolic arrays where dimensions of the array correspond to transitive order relations among entities

More Than Storage of Information: What Working Memory Contributes to Visual Abductive Reasoning

Abductive reasoning is the process of finding the best explanation for a set of observations. As the number of possible observations and corresponding explanations may be very high, it is commonly accepted that working memory capacity is closely related to successful abductive reasoning. However, the precise relationship between abductive reasoning and working memory capacity remains largely opaque. In a reanalysis of two experiments (N = 59), we first investigated whether reasoning performance is associated with differences in working memory capacity. Second, using eye tracking, we explored the relationship between the facets of working memory and the process of visuospatial reasoning. We used working memory tests of both components (verbal-numerical/spatial) as well as an intelligence measure. Results showed a clear relationship between reasoning accuracy and spatial components as well as intelligence. Process measures suggested that working memory seems to be a limiting factor to reasoning and that looking-back to previously relevant areas is compensating for poor mental models rather than being a sign of a particularly elaborate one. Following, high working memory ability might lead to the use of strategies to optimize the content and complexity of the mental representation on which abductive reasoning is based

StepGame: A New Benchmark for Robust Multi-Hop Spatial Reasoning in Texts

Inferring spatial relations in natural language is a crucial abil- ity an intelligent system should possess. The bAbI dataset tries to capture tasks relevant to this domain (task 17 and 19). However, these tasks have several limitations. Most impor- tantly, they are limited to fixed expressions, they are limited in the number of reasoning steps required to solve them, and they fail to test the robustness of models to input that contains irrelevant or redundant information. In this paper, we present a new Question-Answering dataset called StepGame for ro- bust multi-hop spatial reasoning in texts. Our experiments demonstrate that state-of-the-art models on the bAbI dataset struggle on the StepGame dataset. Moreover, we propose a Tensor-Product based Memory-Augmented Neural Network (TP-MANN) specialized for spatial reasoning tasks. Experi- mental results on both datasets show that our model outper- forms all the baselines with superior generalization and ro- bustness performance

Knowledge and Reasoning in Spatial Analysis

Reasoning is an essential part of any analysis process. Especially in visual analytics, the quality of the results depends heavily on the knowledge and reasoning skills of the analyst. In this study, we consider how to make the results transparent by visualizing the reasoning and the knowledge, so that persons from outside can trace and verify them. The focus of this study is in spatial analysis and a case study was carried out on a process of off-road mobility analysis. In the case study, linked views of a map and a PCP were identified as reasoning artifacts. The knowledge used by the analyst was formed by these artifacts and the tangible pieces of information identified in them, along with the mental models of the analyst′s mind. To make the results transparent, the tangible pieces of information were marked with sketches and the mental models were presented in causal graphs because it was found that causality was central to the reasoning process in the case study. The causal graph allows the reasoning of the analyst to be studied, as well as traced back to its origin.Peer reviewe

3D decomposition as a spatial reasoning process: A window to 1st grade students’ spatial structuring

3D decomposition is considered a spatial reasoning process (Davis et al., 2015). Spatial structuring is a form of abstraction that creates mental models of shapes’ structures (Battista & Clements, 1996). Since early grades, both play an important role in understanding shapes’ structures and in learning how to manipulate them flexibly and fluently. 3D shapes have a strong presence in early grades, yet there is still little research about the way students learn their structures. We seek to answer the following questions: How do 1st graders decompose 3D shapes? How are these decompositions related to spatial structuring?info:eu-repo/semantics/publishedVersio

Visual Imagery in Deductive Reasoning: Results from experiments with sighted, blindfolded, and congenitally totally blind persons

We report three experiments on visual mental imagery in de-ductive reasoning. Reasoning performance of sighted partici-pants was impeded if the materials were easy to envisage as visual mental images. Congenitally totally blind participants did not show this visual-impedance effect. Blindfolded par-ticipants with normal vision showed the same pattern of per-formance as the sighted. We conclude that irrelevant visual detail can be a nuisance in reasoning and impedes the process

A computational theory of temporal inference

We describe a novel model-based theory of how individuals reason deductively about temporal relations. It positsthat temporal assertions refer to mental models – iconic representations of possibilities – of events (Khemlani, Harrison, &Trafton, 2015; Schaeken, Johnson-Laird, & d’Ydewalle, 1996). In line with recent accounts of spatial reasoning (Ragni &Knauff, 2013), the theory posits that individuals tend to build a single preferred model of a temporal description. The moremodels necessary to yield a correct answer, the harder that problem is. The theory is implemented in a computer program,mReasoner, which draws temporal deductions by building models. It varies four separate factors in the process: the size of amodel, its contents, the propensity to consider alternative models, and the propensity to revise initial conclusions. Two studiescorroborated the predictions of the theory and its computational implementation. We conclude by discussing temporal andrelational inference more broadly

Schoolchildren’s transitive reasoning with the spatial relation ‘is left/right of’

We examine schoolchildren’s reasoning with spatial relations, such as ‘is to the left of’. Our aims are to obtain a more precise account of the effect of working memory on reasoning, a more detailed understanding of the internal representation of mental models and a developmental perspective. We discuss two experiments in which 348 children, between eight and twelve years old, needed to verify conclusions for 24 reasoning problems describing the spatial relations between pieces of clothing. In both experiments, children in the experimental condition were allowed to take notes by means of paper and pencil. We find that the participants spontaneously draw iconic representations of the items’ spatial ordering, have a strong preference for only considering one possible state of affairs even when more are relevant, and that an explanation in terms of working memory capacity alone cannot fully explain the data.Multivariate analysis of psychological dat

Factors and processes in children's transitive deductions

Transitive tasks are important for understanding how children develop socio-cognitively. However, developmental research has been restricted largely to questions surrounding maturation. We asked 6-, 7- and 8-year-olds (N = 117) to solve a composite of five different transitive tasks. Tasks included conditions asking about item-C (associated with the marked relation) in addition to the usual case of asking only about item-A (associated with the unmarked relation). Here, children found resolving item-C much easier than resolving item-A, a finding running counter to long-standing assumptions about transitive reasoning. Considering gender perhaps for the first time, boys exhibited higher transitive scores than girls overall. Finally, analysing in the context of one recent and well-specified theory of spatial transitive reasoning, we generated the prediction that reporting the full series should be easier than deducing any one item from that series. This prediction was not upheld. We discuss amendments necessary to accommodate all our earlier findings