Wai-Kiang Yeap

Robotics and Cognitive Approaches to Spatial Mapping

This important work is an attempt to synthesize two areas that need to be treated in tandem. The book brings together the fields of robot spatial mapping and cognitive spatial mapping, which share some common core problems. One would expect some cross-fertilization of research between the two areas to have occurred, yet this has begun only recently. There are now signs that some synthesis is happening, so this work is a timely one for students and engineers in robotics.

Valency based weighted association rule mining

Association rule mining is an important data mining task that discovers relationships among items in a transaction database. Most approaches to association rule mining assume that all items within a dataset have a uniform distribution with respect to support. Therefore, weighted association rule mining (WARM) was introduced to provide a notion of importance to individual items. Previous approaches to the weighted association rule mining problem require users to assign weights to items. This is infeasible when millions of items are present in a dataset. In this paper we propose a method that is based on a novel Valency model that automatically infers item weights based on interactions between items. Our experimentation shows that the weighting scheme results in rules that better capture the natural variation that occurs in a dataset when compared to a miner that does not employ such a weighting scheme.

Using a Mobile Robot to Test a Theory of Cognitive Mapping

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.

The Correspondence Problem in Topological Metric Mapping – Using Absolute Metric Maps to Close Cycles

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.

Visualizing the meaning of texts

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.

Using absolute metric maps to close cycles in a topological map

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.

A Framework for Interpreting Bridging Anaphora

In this paper we present a novel framework for resolving bridging anaphora. We argue that anaphora, particularly bridging anaphora, is used as a shortcut device similar to the use of compound nouns. Hence, the two natural language usage phenomena would have to be based on the same theoretical framework. We use an existing theory on compound nouns to test its validity for anaphora usages. To do this, we used human annotators to interpret indirect anaphora from naturally occurring discourses. The annotators were asked to classify the relations between anaphor-antecedent pairs into relation types that have been previously used to describe the relations between a modifier and the head noun of a compound noun. We obtained very encouraging results with an average Fleiss’s κ value of 0.66 for inter-annotation agreement. The results were evaluated against other similar natural language interpretation annotation experiments and were found to compare well.

Computing the local space of a mobile robot

A popular approach to describing the environment of an autonomous system is to compute a representation for the space surrounding the robot, termed the local space. Recently the focus of much of the work in this area in robotics has been on acquiring a usable representation. To this end many computationally demanding algorithms have been devised in the hope that accurate representations which more closely match the real world will be computed. However this is very difficult to achieve from the robot’s initial experience of its environment. We argue that an inaccurate but useful representation can be computed from the robot’s initial view of the local space. We present an algorithm for computing this initial representation and show its implementation on a robot with sonar sensors.