CAIR - the Centre for Artificial Intelligence Research
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Information Technology is a term that encompasses all forms of technology used to create and manipulate information in its various forms. The primary goal of the Centre for Artificial Intelligence Research (CAIR) is to provide strong leadership and a stimulating environment for the development of these different forms of technology in New Zealand. However, our desire is that our research will not be ivory-towered. Since AUT is the only university of technology in this country, this emphasis is an important one; our research must not only be at the leading edge but must also be practically useful.
Given the widely ranging possibilities of research in this area, the Centre has decided to stay focused in its foundation years. It has, in consultation with the School of Computing and Mathematical Sciences and KEDRI, decided to focus on three main areas of research and development work, namely human language technology, speech technology, and robotics.
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- ItemPronominal Anaphora Resolution Using a Shallow Meaning Representation of Sentences(Springer, 2004) Ho, H.; Min, K.; Yeap, W.This paper describes a knowledge-poor anaphora resolution approach based on a shallow meaning representation of sentences. The structure afforded in such a representation provides immediate identification of local domains which are required for resolving pronominal anaphora. Other kinds of information used include syntactic information, structure parallelism and salience weights. We collected 111 singular 3rd person pronouns from open domain resources such as children's novel and examples from several anaphora resolution papers. There are 111 third-person singular pronouns in the experiment data set and 94 of them demonstrate pronominal anaphora in domain of test data. The system successfully resolves 78.4% of anaphoric examples. © Springer-Verlag Berlin Heidelberg 2004.
- ItemThe Correspondence Problem in Topological Metric Mapping – Using Absolute Metric Maps to Close Cycles(Springer, 2004) Jefferies, M.; Cosgrove, M.; Baker, J.; Yeap, W.In Simultaneous Localisation and Mapping (SLAM) the correspondence problem, specifically detecting cycles, is one of the most difficult challenges for an autonomous mobile robot. In this paper we show how significant cycles in a topological map can be identified with a companion absolute global metric map. A tight coupling of the basic unit of representation in the two maps is the key to the method. Each local space visited is represented, with its own frame of reference, as a node in the topological map. In the global absolute metric map these local space representations from the topological map are described within a single global frame of reference. The method exploits the overlap which occurs when duplicate representations are computed from different vantage points for the same local space. The representations need not be exactly aligned and can thus tolerate a limited amount of accumulated error. We show how false positive overlaps which are the result of a misaligned map, can be discounted. © Springer-Verlag 2004.
- ItemUsing Absolute Metric Maps to Close Cycles in a Topological Map(Springer, 2005) Jefferies, M.; Yeap, W.; Cosgrove, M.; Baker, J.In simultaneous localisation and mapping (SLAM) the correspondence problem, specifically detecting cycles, is one of the most difficult challenges for an autonomous mobile robot. In this paper we show how significant cycles in a topological map can be identified with a companion absolute global metric map. A tight coupling of the basic unit of representation in the two maps is the key to the method. Each local space visited is represented, with its own frame of reference, as a node in the topological map. In the global absolute metric map these local space representations from the topological map are described within a single global frame of reference. The method exploits the overlap which occurs when duplicate representations are computed from different vantage points for the same local space. The representations need not be exactly aligned and can thus tolerate a limited amount of accumulated error. We show how false positive overlaps which are the result of a misaligned map, can be discounted. © 2005 Springer Science+Business Media, Inc.
- ItemVisualizing the Meaning of Texts(IEEE, 2005) Yeap, W.; Reedy, P.; Min, K.; Ho, H.We implemented SmartINFO, an experimental system for the visualization of the meaning of texts. SmartINFO consists of 4 modules: a universal grammar engine (UGE), an anaphora engine, a concept engine and a visualization engine. We discuss two methods of visualizing meanings of text. One approach is a word-centered approach and the other, a clausal-centered approach. © 2005 IEEE.
- ItemComputing a Network of ASRs Using a Mobile Robot Equipped with Sonar Sensors(IEEE, 2006) Wong, C.; Yeap, W.; Schmidt, J.This paper presents a novel algorithm for computing absolute space representations (ASRs) [1]-[2] for mobile robots equipped with sonar sensors and an odometer. The robot is allowed to wander freely (i.e. without following any fixed path) along the corridors in an office environment from a given start point to an end point. It then wanders from the end point back to the start point. The resulting ASRs computed in both directions are shown. © 2006 IEEE.
- ItemA Split & Merge Approach to Metric-Topological Map-Building(IEEE, 2006) Schmidt, J.; Wong, C.; Yeap, W.We present a novel split and merge based method for dividing a given metric map into distinct regions, thus effectively creating a topological map on top of a metric one. The initial metric map is obtained from range data that are convened to a geometric map consisting of linear approximations of the indoor environment. The splitting is done using an objective function that computes the quality of a region, based on criteria such as the average region width (to distinguish big rooms from corridors) and overall direction (which accounts for sharp bends). A regularization term is used in order to avoid the formation of very small regions, which may originate from missing or unreliable sensor data. Experiments based on data acquired by a mobile robot equipped with sonar sensors are presented, which demonstrate the capabilities of the proposed method. © 2006 IEEE.
- ItemFrom Spatial Perception to Cognitive Mapping: How Is the Flow of Information Controlled?(AAAI, 2007) Yeap, W.Most models of cognitive mapping would suggest that the process begins by constructing some form of a structural representation of the environment visited. From the latter representation, one develops a conceptual view of the environment. The flow of information in the process is almost unidirectional, from perception to conception. In this paper, I argue that this process is inappropriate for a human cognitive mapping process. The latter process should begin with some symbolic notions of places and never needed to construct explicitly a structural representation of the environment visited. Humans' ability to visualise the structural details in a familiar environment comes from the increasingly detailed grounding of its symbols to the real world as a result of familiarisation and attention to details.
- ItemSpatial Information Extraction for Cognitive Mapping with a Mobile Robot(Springer, 2007) Schmidt, J.; Wong, C.; Yeap, W.When animals (including humans) first explore a new environment, what they remember is fragmentary knowledge about the places visited. Yet, they have to use such fragmentary knowledge to find their way home. Humans naturally use more powerful heuristics while lower animals have shown to develop a variety of methods that tend to utilize two key pieces of information, namely distance and orientation information. Their methods differ depending on how they sense their environment. Could a mobile robot be used to investigate the nature of such a process, commonly referred to in the psychological literature as cognitive mapping? What might be computed in the initial explorations and how is the resulting "cognitive map" be used for localization? In this paper, we present an approach using a mobile robot to generate a "cognitive map", the main focus being on experiments conducted in large spaces that the robot cannot apprehend at once due to the very limited range of its sensors. The robot computes a "cognitive map" and uses distance and orientation information for localization. © Springer-Verlag Berlin Heidelberg 2007.
- ItemUsing a Mobile Robot to Test a Theory of Cognitive Mapping(Springer, 2008) Yeap, W.; Wong, C.; Schmidt, J.This paper describes using a mobile robot, equipped with some sonar sensors and an odometer, to test navigation through the use of a cognitive map. The robot explores an office environment, computes a cognitive map, which is a network of ASRs [36, 35], and attempts to find its way home. Ten trials were conducted and the robot found its way home each time. From four random positions in two trials, the robot estimated the home position relative to its current position reasonably accurately. Our robot does not solve the simultaneous localization and mapping problem and the map computed is fuzzy and inaccurate with much of the details missing. In each homeward journey, it computes a new cognitive map of the same part of the environment, as seen from the perspective of the homeward journey. We show how the robot uses distance information from both maps to find its way home. © 2007 Springer-Verlag Berlin Heidelberg.
- ItemAutonomous Robot Mapping by Landmark Association(CEUR-WS.org, 2015) Azizzul, Z; Yeap, W; Airenti, G; Bara, BG; Sandini, GThis paper shows how an indoor mobile robot equipped with a laser sensor and an odometer computes its global map by associating landmarks found in the environment. The approach developed is based on the observation that humans and animals detects where they are in the surrounding by comparing their spatial relation to some known or recognized objects in the environments, i.e. landmarks. In this case, landmarks are defined as 2D surfaces detected in the robot’s surroundings. They are recognised if they are detected in two successive views. From a cognitive standpoint, this work is inspired by two assumptions about the world; (a) the world is relatively stable and (2) there is a significant overlap of spatial information between successive views. In the implementation, the global map is first initialised with the robot’s first view, and then updated each time landmarks are found at every two successive views. The difference here is, where most robot mapping work integrates everything they see in their update, this work takes advantage of updating only the landmarks before adding the nearby objects associated with them. By association, the map is built without error corrections and the final map produced is not metrically precise.