The aim of the Angry Birds AI competition (AIBIRDS) is to build intelligent agents that can play new Angry Birds levels better than the best human players. This is surprisingly difficult for AI as it requires similar capabilities to what intelligent systems need for successfully interacting with the physical world, one of the grand challenges of AI. As such the competition offers a simplified and controlled environment for developing and testing the necessary AI technologies, a seamless integration of computer vision, machine learning, knowledge representation and reasoning, reasoning under uncertainty, planning, and heuristic search, among others. Over the past three years there have been significant improvements, but we are still a long way from reaching the ultimate aim and, thus, there are great opportunities for participants in this competition.

RCC8 is a constraint language that serves for qualitative spatial representation and reasoning by encoding the topological relations between spatial entities. We focus on efficiently characterizing non-redundant constraints in large real world RCC8 networks and obtaining their prime networks. For a RCC8 network N a constraint is redundant, if removing that constraint from N does not change the solution set of N. A prime network of N is a network which contains no redundant constraints, but has the same solution set as N. We make use of a particular partial consistency, namely, G-path consistency, and obtain new complexity results for various cases of RCC8 networks, while we also show that given a maximal distributive subclass for RCC8 and a network N defined on that subclass, the prunning capacity of G-path consistency and path consistency is identical on the common edges of G and the complete graph of N, when G is a triangulation of the constraint graph of N. Finally, we devise an algorithm based on G-path consistency to compute the unique prime network of a RCC8 network, and show that it significantly progresses the state-of-the-art for practical reasoning with real RCC8 networks scaling up to millions of nodes.

We introduce a framework for qualitative reasoning about directions in high-dimensional spaces, called EER, where our main motivation is to develop a form of commonsense reasoning about semantic spaces. The proposed framework is, however, more general; we show how qualitative spatial reasoning about points with several existing calculi can be reduced to the realisability problem for EER (or REER for short), including LR and calculi for reasoning about betweenness, collinearity and parallelism. Finally, we propose an efficient but incomplete inference method, and show its effectiveness for reasoning with EER as well as reasoning with some of the aforementioned calculi.

Clustering has gained widespread use, especially for static data. However, the rapid growth of spatio-temporal data from numerous instruments, such as earth-orbiting satellites, has created a need for spatio-temporal clustering methods to extract and monitor dynamic clusters. Dynamic spatio-temporal clustering faces two major challenges: First, the clusters are dynamic and may change in size, shape, and statistical properties over time. Second, numerous spatio-temporal data are incomplete, noisy, heterogeneous, and highly variable (over space and time). We propose a new spatio-temporal data mining paradigm, to autonomously identify dynamic spatio-temporal clusters in the presence of noise and missing data. Our proposed approach is more robust than traditional clustering and image segmentation techniques in the case of dynamic patterns, non-stationary, heterogeneity, and missing data. We demonstrate our method's performance on a real-world application of monitoring in-land water bodies on a global scale.

Qualitative spatial reasoning deals with relational spatial knowledge and with how this knowledge can be processed efficiently. Identifying suitable representations for spatial knowledge and checking whether the given knowledge is consistent has been the main research focus in the past two decades. However, where the spatial information comes from, what kind of information can be obtained and how it can be obtained has been largely ignored. This paper is an attempt to start filling this gap. We present a method for extracting detailed spatial information from sensor measurements of regions. We analyse how different sparse sensor measurements can be integrated and what spatial information can be extracted from sensor measurements. Different from previous approaches to qualitative spatial reasoning, our method allows us to obtain detailed information about the internal structure of regions. The result has practical implications, for example, in disaster management scenarios, which include identifying the safe zones in bushfire and flood regions.

This paper introduces a new qualitative spatial reasoning formalism, called   Interval Occlusion  Calculus (IOC), that takes into account multiple (distinct) viewpoints of a scene. This formalism extends  Allen's Interval Algebra by including an interval-based definition for spatial occlusion.

Object recognition systems can be unreliable when run in isolation depending on only image based features, but their performance can be improved when taking scene context into account. In this paper, we present techniques to model and infer object labels in real scenes based on a variety of spatial relations — geometric features which capture how objects co-occur — and compare their efficacy in the context of augmenting perception based object classification in real-world table-top scenes. We utilise a long-term dataset of office table-tops for qualitatively comparing the performances of these techniques. On this dataset, we show that more intricate techniques, have a superior performance but do not generalise well on small training data. We also show that techniques using coarser information perform crudely but sufficiently well in standalone scenarios and generalise well on small training data. We conclude the paper, expanding on the insights we have gained through these comparisons and comment on a few fundamental topics with respect to long-term autonomous robots.

Robots are increasingly becoming key players in human-robot teams. To become effective teammates, robots must possess profound understanding of an environment, be able to reason about the desired commands and goals within a specific context, and be able to communicate with human teammates in a clear and natural way. To address these challenges, we have developed an intelligence architecture that combines cognitive components to carry out high-level cognitive tasks, semantic perception to label regions in the world, and a natural language component to reason about the command and its relationship to the objects in the world. This paper describes recent developments using this architecture on a fielded mobile robot platform operating in unknown urban environments. We report a summary of extensive outdoor experiments; the results suggest that a multidisciplinary approach to robotics has the potential to create competent human-robot teams.

This paper is concerned with extension of AngledCORE-9 by Sokeh, Gould, and Renz, a comprehensive representation of spatial information that can be efficiently extracted from interacting objects present in video using their approximated bounding box. Spatial information is important for identification of relation between multiple objects, hence the work is a step forward for tasks such as semantics content analysis and visual information access. To that end we present an approach to incorporating the spatiotemporal volume of objects into AngledCORE-9. The approach is able to detect, track and segment object volumes from a video stream, based on which spatial information is identified in an efficient manner. Accurate spatial and temporal information can be obtained by precise representation of the shape region and the oriented bounding box. A human action classification task is adopted in order to assess the performance of the approach. The experiment with two challenging datasets indicates that the outcome of this approach is comparable to the state-of-the-art.

This paper presents a novel approach to modeling the dynamics of human movements with a grid-based representation.For each grid cell, we formulate the local dynamics using a variant of the left-to-right HMM, and thus explicitly model the exiting direction from the current cell. The dependency of this process on the entry direction is captured by employing the Input-Output HMM (IOHMM). On a higher level, we introduce the place where the whole trajectory originated into the IOHMM framework forming a hierarchical input structure. Therefore, we manage to capture both local spatial-temporal correlations and the long-term dependency on faraway initiating events, thus enabling the developed model to incorporate more information and to generate more informative predictions of future trajectories.The experimental results in an office corridor environment verify the capabilities of our method.