Computer Science and Metaphysics: A Cross-Fertilization

Computational philosophy is the use of mechanized computational techniques to unearth philosophical insights that are either difficult or impossible to find using traditional philosophical methods. Computational metaphysics is computational philosophy with a focus on metaphysics. In this paper, we (a) develop results in modal metaphysics whose discovery was computer assisted, and (b) conclude that these results work not only to the obvious benefit of philosophy but also, less obviously, to the benefit of computer science, since the new computational techniques that led to these results may be more broadly applicable within computer science. The paper includes a description of our background methodology and how it evolved, and a discussion of our new results.Comment: 39 pages, 3 figure

Mechanizing Principia Logico-Metaphysica in Functional Type Theory

Principia Logico-Metaphysica contains a foundational logical theory for metaphysics, mathematics, and the sciences. It includes a canonical development of Abstract Object Theory [AOT], a metaphysical theory (inspired by ideas of Ernst Mally, formalized by Zalta) that distinguishes between ordinary and abstract objects. This article reports on recent work in which AOT has been successfully represented and partly automated in the proof assistant system Isabelle/HOL. Initial experiments within this framework reveal a crucial but overlooked fact: a deeply-rooted and known paradox is reintroduced in AOT when the logic of complex terms is simply adjoined to AOT's specially-formulated comprehension principle for relations. This result constitutes a new and important paradox, given how much expressive and analytic power is contributed by having the two kinds of complex terms in the system. Its discovery is the highlight of our joint project and provides strong evidence for a new kind of scientific practice in philosophy, namely, computational metaphysics. Our results were made technically possible by a suitable adaptation of Benzm\"uller's metalogical approach to universal reasoning by semantically embedding theories in classical higher-order logic. This approach enables one to reuse state-of-the-art higher-order proof assistants, such as Isabelle/HOL, for mechanizing and experimentally exploring challenging logics and theories such as AOT. Our results also provide a fresh perspective on the question of whether relational type theory or functional type theory better serves as a foundation for logic and metaphysics.Comment: 14 pages, 6 figures; preprint of article with same title to appear in The Review of Symbolic Logi

An assessment of key model parametric uncertainties in projections of Greenland Ice Sheet behavior

Lack of knowledge about the values of ice sheet model input parameters introduces substantial uncertainty into projections of Greenland Ice Sheet contributions to future sea level rise. Computer models of ice sheet behavior provide one of several means of estimating future sea level rise due to mass loss from ice sheets. Such models have many input parameters whose values are not well known. Recent studies have investigated the effects of these parameters on model output, but the range of potential future sea level increases due to model parametric uncertainty has not been characterized. Here, we demonstrate that this range is large, using a 100-member perturbed-physics ensemble with the SICOPOLIS ice sheet model. Each model run is spun up over 125 000 yr using geological forcings and subsequently driven into the future using an asymptotically increasing air temperature anomaly curve. All modeled ice sheets lose mass after 2005 AD. Parameters controlling surface melt dominate the model response to temperature change. After culling the ensemble to include only members that give reasonable ice volumes in 2005 AD, the range of projected sea level rise values in 2100 AD is ~40 % or more of the median. Data on past ice sheet behavior can help reduce this uncertainty, but none of our ensemble members produces a reasonable ice volume change during the mid-Holocene, relative to the present. This problem suggests that the model's exponential relation between temperature and precipitation does not hold during the Holocene, or that the central-Greenland temperature forcing curve used to drive the model is not representative of conditions around the ice margin at this time (among other possibilities). Our simulations also lack certain observed physical processes that may tend to enhance the real ice sheet's response. Regardless, this work has implications for other studies that use ice sheet models to project or hindcast the behavior of the Greenland Ice Sheet

Robust and efficient people detection with 3-D range data using shape matching

Information about the location of a person is a necessity for Human-Robot Interaction (HRI) as it enables the robot to make human aware decisions and facilitates the extraction of further useful information; such as low-level gestures and gaze. This paper presents a robust method for person detection with 3-D range data using shape matching. Projections of the 3-D data onto 2-D planes are exploited to effectively and efficiently represent the data for scene segmentation and shape extraction. Fourier descriptors (FD) are used to describe the shapes and are subsequently classified with a Support Vector Machine (SVM). A database of 25 people was collected and used to test this approach. The results show that the computationally efficient shape features can be used to robustly detect the location of people

Robust manipulability-centric object detection in time-of-flight camera point clouds

This paper presents a method for robustly identifying the manipulability of objects in a scene based on the capabilities of the manipulator. The method uses a directed histogram search of a time-of-flight camera generated 3D point cloud that exploits the logical connection between objects and the respective supporting surface to facilitate scene segmentation. Once segmented the points above the supporting surface are searched, again with a directed histogram, and potentially manipulatable objects identified. Finally, the manipulatable objects in the scene are identified as those from the potential objects set that are within the manipulators capabilities. It is shown empirically that the method robustly detects the supporting surface with ±15mm accuracy and successfully discriminates between graspable and non-graspable objects in cluttered and complex scenes

Bridge maintenance robotic arm: Capacitive sensor for obstacle ranging in particle laden air

This paper describes an Adaptive Capacitive Sensor Network for Obstacle Ranging (ACSOR) that is intended to provide entire arm encompassing obstacle range data for a robotic arm conducting the task of sandblasting a bridge. A multi-channel capacitive sensor capable of dynamic obstacle ranging in air heavily laden with lead contaminated sandblasting refuse has been developed. Experimental results have shown the ACSOR's working range to be 50cm, that it is relatively immune from airborne lead contaminated sandblasting refuse and that it is capable of ranging an obstacle 21cm away whilst fitted to a robotic arm moving at 2cm/s with an obstacle range error of less than 1cm

Bootstrapping navigation and path planning using human positional traces

Navigating and path planning in environments with limited a priori knowledge is a fundamental challenge for mobile robots. Robots operating in human-occupied environments must also respect sociocontextual boundaries such as personal workspaces. There is a need for robots to be able to navigate in such environments without having to explore and build an intricate representation of the world. In this paper, a method for supplementing directly observed environmental information with indirect observations of occupied space is presented. The proposed approach enables the online inclusion of novel human positional traces and environment information into a probabilistic framework for path planning. Encapsulation of sociocontextual information, such as identifying areas that people tend to use to move through the environment, is inherently achieved without supervised learning or labelling. Our method bootstraps navigation with indirectly observed sensor data, and leverages the flexibility of the Gaussian process (GP) for producing a navigational map that sampling based path planers such as Probabilistic Roadmaps (PRM) can effectively utilise. Empirical results on a mobile platform demonstrate that a robot can efficiently and socially-appropriately reach a desired goal by exploiting the navigational map in our Bayesian statistical framework. © 2013 IEEE