Optimistic linear programming gives logarithmic regret for irreducible MDPs

We present an algorithm called Optimistic Linear Programming (OLP) for learning to optimize average reward in an irreducible but otherwise unknown Markov decision process (MDP). OLP uses its experience so far to estimate the MDP. It chooses actions by optimistically maximizing estimated future rewards over a set of next-state transition probabilities that are close to the estimates, a computation that corresponds to solving linear programs. We show that the total expected reward obtained by OLP up to time T is within C(P) log T of the reward obtained by the optimal policy, where C(P) is an explicit, MDP-dependent constant. OLP is closely related to an algorithm proposed by Burnetas and Katehakis with four key differences: OLP is simpler, it does not require knowledge of the supports of transition probabilities, the proof of the regret bound is simpler, but our regret bound is a constant factor larger than the regret of their algorithm. OLP is also similar in flavor to an algorithm recently proposed by Auer and Ortner. But OLP is simpler and its regret bound has a better dependence on the size of the MDP.

CAT Detect (Computer Activity Timeline Detection) : a tool for detecting inconsistency in computer activity timelines

The construction of timelines of computer activity is a part of many digital investigations. These timelines of events are composed of traces of historical activity drawn from system logs and potentially from evidence of events found in the computer file system. A potential problem with the use of such information is that some of it may be inconsistent and contradictory thus compromising its value. This work introduces a software tool (CAT Detect) for the detection of inconsistency within timelines of computer activity. We examine the impact of deliberate tampering through experiments conducted with our prototype software tool. Based on the results of these experiments, we discuss techniques which can be employed to deal with such temporal inconsistencies.

Food(ing): between human-computer and human-food-experience

This workshop is a continuation and extension to the successful past workshops exploring the intersection of food, technology, place, and people, namely 2009 OZCHI workshop, Hungry 24/7? HCI Design for Sustainable Food Culture and Sustainable Interaction with Food, Technology, and the City [1] and 2010 CHI panel Making Food, Producing Sustainability [3]. The workshop aims to bring together experts from diverse backgrounds including academia, government, industry, and non-for-profit organisations. It specifically aims to create a space for discussion and design of innovative approaches to understanding and cultivating sustainable food practices via human-computer-interaction (HCI) as well as addressing the wider opportunities for the HCI community to engage with food as a key issue for sustainability The workshop addresses environmental, health, and social domains of sustainability in particular, by looking at various conceptual and design approaches in orchestrating sustainable interaction of people and food in and through dynamic techno-social networks.

Evolving noisy oscillatory dynamics in genetic regulatory networks

We introduce a genetic programming (GP) approach for evolving genetic networks that demonstrate desired dynamics when simulated as a discrete stochastic process. Our representation of genetic networks is based on a biochemical reaction model including key elements such as transcription, translation and post-translational modifications. The stochastic, reaction-based GP system is similar but not identical with algorithmic chemistries. We evolved genetic networks with noisy oscillatory dynamics. The results show the practicality of evolving particular dynamics in gene regulatory networks when modelled with intrinsic noise.

Computer-intensive simulations for cellular models

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Order of instruction effects - do they make a difference when teaching senior chemistry with computer based visualizations?

This study investigated whether conceptual development is greater if students learning senior chemistry hear teacher explanations and other traditional teaching approaches first then see computer based visualizations or vice versa. Five Canadian chemistry classes, taught by three different teachers, studied the topics of Le Chatelier’s Principle and dynamic chemical equilibria using scientific visualizations with the explanation and visualizations in different orders. Conceptual development was measured using a 12 item test based on the Chemistry Concepts Inventory. Data was obtained about the students’ abilities, learning styles (auditory, visual or kinesthetic) and sex, and the relationships between these factors and conceptual development due to the teaching sequences were investigated. It was found that teaching sequence is not important in terms of students’ conceptual learning gains, across the whole cohort or for any of the three subgroups.

Emergence in games

The future direction of game development is towards more flexible, realistic, and interactive game worlds. However, current methods of game design do not allow for anything other than pre-scripted player exchanges and static objects and environments. An emergent approach to game development involves the creation of a globally designed game system that provides rules and boundaries for player interactions, rather than prescribed paths. Emergence in Games provides a detailed foundation for applying the theory and practice of emergence in games to game design. Emergent narrative, characters and agents, and game worlds are covered and a hands-on tutorial and case study allow the reader to the put the skills and ideas presented into practice.

Letter to the editor : likelihood of tetraplegia after CT clearance of the cervical spine in obtunded blunt trauma patients

...the probabilistic computer simulation study by Dunham and colleagues evaluating the impact of different cervical spine management (CSM) strategies on tetraplegia and brain injury outcomes.1 Based on literature findings, expert opinion and with use of advances programming techniques the authors conclude that early collar removal without cervical spine magnetic resonance imaging (MRI) is a preferable CSM strategy for comatose, blunt trauma patients with extremity movement and a negative cervical spine computed tomography(CT) scan. Although we do not have the required expertise to comment on the applied statistical approach, we would like to comment on one of the medical assumptions raised by the authors, namely the likelihood of tetraplegia in this specific population....

Measuring the effectiveness of computer-based scientific visualisations for conceptual development in Australian chemistry classrooms

Visual modes of representation have always been very important in science and science education. Interactive computer-based animations and simulations offer new visual resources for chemistry education. Many studies have shown that students enjoy learning with visualisations but few have explored how learning outcomes compare when teaching with or without visualisations. This study employs a quasi-experimental crossover research design and quantitative methods to measure the educational effectiveness - defined as level of conceptual development on the part of students - of using computer-based scientific visualisations versus teaching without visualisations in teaching chemistry. In addition to finding that teaching with visualisations offered outcomes that were not significantly different from teaching without visualisations, the study also explored differences in outcomes for male and female students, students with different learning styles (visual, aural, kinesthetic) and students of differing levels of academic ability.

The Fifth 48 Hour Game Making Challenge (2011)

The 48-hour game making challenge started out in 2007 as a creative community event. We have run this event each year since and seen over 120 games made. 2011 was the most remarkable in that each of the 20 teams made a playable game – the shape of the challenge has changed …. We have invested in the process of reflective practice & action research, with the event being part of a sweep of programs that inform this research, with each year giving us fresh insights into both the creative practice and essential concerns, process and trends of the independent games industry creative community, which we then respond to within our curatorial development of the subsequent programming. The 2011 48-hour challenge research project focused on the people and the site. We were specifically interested in the manner in which the community occupied the creative space.

Towards automatic object segmentation with sequential multiple views

Object segmentation is one of the fundamental steps for a number of robotic applications such as manipulation, object detection, and obstacle avoidance. This paper proposes a visual method for incorporating colour and depth information from sequential multiview stereo images to segment objects of interest from complex and cluttered environments. Rather than segmenting objects using information from a single frame in the sequence, we incorporate information from neighbouring views to increase the reliability of the information and improve the overall segmentation result. Specifically, dense depth information of a scene is computed using multiple view stereo. Depths from neighbouring views are reprojected into the reference frame to be segmented compensating for imperfect depth computations for individual frames. The multiple depth layers are then combined with color information from the reference frame to create a Markov random field to model the segmentation problem. Finally, graphcut optimisation is employed to infer pixels belonging to the object to be segmented. The segmentation accuracy is evaluated over images from an outdoor video sequence demonstrating the viability for automatic object segmentation for mobile robots using monocular cameras as a primary sensor.

The RatSLAM project : robot spatial navigation

Rats are superior to the most advanced robots when it comes to creating and exploiting spatial representations. A wild rat can have a foraging range of hundreds of meters, possibly kilometers, and yet the rodent can unerringly return to its home after each foraging mission, and return to profitable foraging locations at a later date (Davis, et al., 1948). The rat runs through undergrowth and pipes with few distal landmarks, along paths where the visual, textural, and olfactory appearance constantly change (Hardy and Taylor, 1980; Recht, 1988). Despite these challenges the rat builds, maintains, and exploits internal representations of large areas of the real world throughout its two to three year lifetime. While algorithms exist that allow robots to build maps, the questions of how to maintain those maps and how to handle change in appearance over time remain open. The robotic approach to map building has been dominated by algorithms that optimise the geometry of the map based on measurements of distances to features. In a robotic approach, measurements of distance to features are taken with range-measuring devices such as laser range finders or ultrasound sensors, and in some cases estimates of depth from visual information. The features are incorporated into the map based on previous readings of other features in view and estimates of self-motion. The algorithms explicitly model the uncertainty in measurements of range and the measurement of self-motion, and use probability theory to find optimal solutions for the geometric configuration of the map features (Dissanayake, et al., 2001; Thrun and Leonard, 2008). Some of the results from the application of these algorithms have been impressive, ranging from three-dimensional maps of large urban strucutures (Thrun and Montemerlo, 2006) to natural environments (Montemerlo, et al., 2003).

Using strategic movement to calibrate a neural compass : a spiking network for tracking head direction in rats and robots

The head direction (HD) system in mammals contains neurons that fire to represent the direction the animal is facing in its environment. The ability of these cells to reliably track head direction even after the removal of external sensory cues implies that the HD system is calibrated to function effectively using just internal (proprioceptive and vestibular) inputs. Rat pups and other infant mammals display stereotypical warm-up movements prior to locomotion in novel environments, and similar warm-up movements are seen in adult mammals with certain brain lesion-induced motor impairments. In this study we propose that synaptic learning mechanisms, in conjunction with appropriate movement strategies based on warm-up movements, can calibrate the HD system so that it functions effectively even in darkness. To examine the link between physical embodiment and neural control, and to determine that the system is robust to real-world phenomena, we implemented the synaptic mechanisms in a spiking neural network and tested it on a mobile robot platform. Results show that the combination of the synaptic learning mechanisms and warm-up movements are able to reliably calibrate the HD system so that it accurately tracks real-world head direction, and that calibration breaks down in systematic ways if certain movements are omitted. This work confirms that targeted, embodied behaviour can be used to calibrate neural systems, demonstrates that ‘grounding’ of modeled biological processes in the real world can reveal underlying functional principles (supporting the importance of robotics to biology), and proposes a functional role for stereotypical behaviours seen in infant mammals and those animals with certain motor deficits. We conjecture that these calibration principles may extend to the calibration of other neural systems involved in motion tracking and the representation of space, such as grid cells in entorhinal cortex.

Lingodroids : studies in spatial cognition and language

The Lingodroids are a pair of mobile robots that evolve a language for places and relationships between places (based on distance and direction). Each robot in these studies has its own understanding of the layout of the world, based on its unique experiences and exploration of the environment. Despite having different internal representations of the world, the robots are able to develop a common lexicon for places, and then use simple sentences to explain and understand relationships between places even places that they could not physically experience, such as areas behind closed doors. By learning the language, the robots are able to develop representations for places that are inaccessible to them, and later, when the doors are opened, use those representations to perform goal-directed behavior.

Is anybody there? Bootstrapping attendance with engagement

The report card for the introductory programming unit at our university has historically been unremarkable in terms of attendance rates, student success rates and student retention in both the unit and the degree course. After a course restructure recently involving a fresh approach to introducing programming, we reported a high retention in the unit, with consistently high attendance and a very low failure rate. Following those encouraging results, we collected student attendance data for several semesters and compared attendance rates to student results. We have found that interesting workshop material which directly relates to course-relevant assessment items and therefore drives the learning, in an engaging collaborative learning environment has improved attendance to an extraordinary extent, with student failure rates plummeting to the lowest in recorded history at our university.