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.

The Angry Birds AI Competition (aibirds.org) has been held in conjunction with the AI 2012, IJCAI 2013 and ECAI 2014 conferences and will be held again at the IJCAI 2015 conference. The declared goal of the competition is to build an AI agent that can play Angry Birds as good or better than the best human players. In this paper we describe why this is a very difficult problem, why it is a challenge for AI, and why it is an important step towards building AI that can successfully interact with the real world. We also summarise some highlights of past competitions, describe which methods were successful, and give an outlook to proposed variants of the competition. 

Angry Birds is a popular video game where the task is to kill pigs protected by a structure composed of different building blocks that observe the laws of physics. The structure can be destroyed by shooting the angrybirds at it. The fewer birds we use and the more blocks we destroy, the higher the score. One approach to solve the game is by analysing the structure and identifying its strength and weaknesses. This can then be used to decide where to hit the structure with the birds. In this paper we use a qualitative spatial reasoning approach for this task. We develop a novel qualitative spatial calculus for representing and analysing the structure. Our calculus allows us to express and evaluate structural properties and rules, and to infer for each building block which of these properties and rules are satisfied. We use this to compute a heuristic value for each block that corresponds to how useful it is to hit that block. We evaluate our approach by comparing the suggested shot with other possible shots.

Vast amounts of video data are available on the weband are being generated daily using surveillancecameras or other sources. Being able to efficientlyanalyse and process this data is essential for a numberof different applications. We want to be ableto efficiently detect activities in these videos or beable to extract and store essential information containedin these videos for future use and easy searchand access. Cohn et al. (2012) proposed a comprehensiverepresentation of spatial features that canbe efficiently extracted from video and used forthese purposes. In this paper, we present a modifiedversion of this approach that is equally efficientand allows us to extract spatial informationwith much higher accuracy than previously possible.We present efficient algorithms both for extractingand storing spatial information from video,as well as for processing this information in orderto obtain useful spatial features. We evaluate ourapproach and demonstrate that the extracted spatialinformation is considerably more accurate than thatobtained from existing approaches.

The next challenge in qualitative spatial and temporal reasoning is to develop calculi that deal with different aspects of space and time. One approach to achieve this is to combine existing calculi that cover the different aspects. This, however, can lead to calculi that have a very large number of relations and it is a matter of ongoing discussions within the research community whether such large calculi are too large to be useful. In this paper we develop a procedure for reasoning about some of the largest known calculi, the Rectangle Algebra and the Block Algebra with about 10 661  relations. We demonstrate that reasoning over these calculi is possible and can be done efficiently in many cases. This is a clear indication that one of the main goals of the field can be achieved: highly expressive spatial and temporal representations that support efficient reasoning.

The AAAI-07 workshop program was held Sunday and Monday, July 22-23, in Vancouver, British Columbia, Canada. The program included the following thirteen workshops: (1) Acquiring Planning Knowledge via Demonstration; (2) Configuration; (3) Evaluating Architectures for Intelligence; (4) Evaluation Methods for Machine Learning; (5) Explanation-Aware Computing; (6) Human Implications of Human-Robot Interaction; (7) Intelligent Techniques for Web Personalization; (8) Plan, Activity, and Intent Recognition; (9) Preference Handling for Artificial Intelligence; (10) Semantic e-Science; (11) Spatial and Temporal Reasoning; (12) Trading Agent Design and Analysis; and (13) Information Integration on the Web.

The AAAI-07 workshop program was held Sunday and Monday, July 22-23, in Vancouver, British Columbia, Canada. The program included the following thirteen workshops: (1) Acquiring Planning Knowledge via Demonstration; (2) Configuration; (3) Evaluating Architectures for Intelligence; (4) Evaluation Methods for Machine Learning; (5) Explanation-Aware Computing; (6) Human Implications of Human-Robot Interaction; (7) Intelligent Techniques for Web Personalization; (8) Plan, Activity, and Intent Recognition; (9) Preference Handling for Artificial Intelligence; (10) Semantic e-Science; (11) Spatial and Temporal Reasoning; (12) Trading Agent Design and Analysis; and (13) Information Integration on the Web.

The Association for the Advancement of Artificial Intelligence, in cooperation with Stanford University's Department of Computer Science, presented the 2003 Spring Symposium Series, Monday through Wednesday, 24-26 March 2003, at Stanford University. The titles of the eight symposia were Agent-Mediated Knowledge Management, Computational Synthesis: From Basic Building Blocks to High- Level Functions, Foundations and Applications of Spatiotemporal Reasoning (FASTR), Human Interaction with Autonomous Systems in Complex Environments, Intelligent Multimedia Knowledge Management, Logical Formalization of Commonsense Reasoning, Natural Language Generation in Spoken and Written Dialogue, and New Directions in Question-Answering Motivation.

The Association for the Advancement of Artificial Intelligence, in cooperation with Stanford University's Department of Computer Science, presented the 2003 Spring Symposium Series, Monday through Wednesday, 24-26 March 2003, at Stanford University. The titles of the eight symposia were Agent-Mediated Knowledge Management, Computational Synthesis: From Basic Building Blocks to High- Level Functions, Foundations and Applications of Spatiotemporal Reasoning (FASTR), Human Interaction with Autonomous Systems in Complex Environments, Intelligent Multimedia Knowledge Management, Logical Formalization of Commonsense Reasoning, Natural Language Generation in Spoken and Written Dialogue, and New Directions in Question-Answering Motivation.