Proving Induction

The hard problem of induction is to argue without begging the question that inductive inference, applied properly in the proper circumstances, is conducive to truth. A recent theorem seems to show that the hard problem has a deductive solution. The theorem, provable in ZFC, states that a predictive function M exists with the following property: whatever world we live in, M ncorrectly predicts the world’s present state given its previous states at all times apart from a well-ordered subset. On the usual model of time a well-ordered subset is small relative to the set of all times. M’s existence therefore seems to provide a solution to the hard problem. My paper argues for two conclusions. First, the theorem does not solve the hard problem of induction. More positively though, it solves a version of the problem in which the structure of time is given modulo our choice of set theory

A Trusted Infrastructure for Symbolic Analysis of Event-Driven Web Applications

We introduce a trusted infrastructure for the symbolic analysis of modern event-driven Web applications. This infrastructure consists of reference implementations of the DOM Core Level 1, DOM UI Events, JavaScript Promises and the JavaScript async/await APIs, all underpinned by a simple Core Event Semantics which is sufficiently expressive to describe the event models underlying these APIs. Our reference implementations are trustworthy in that three follow the appropriate standards line-by-line and all are thoroughly tested against the official test-suites, passing all the applicable tests. Using the Core Event Semantics and the reference implementations, we develop JaVerT.Click, a symbolic execution tool for JavaScript that, for the first time, supports reasoning about JavaScript programs that use multiple event-related APIs. We demonstrate the viability of JaVerT.Click by proving both the presence and absence of bugs in real-world JavaScript code

A trusted infrastructure for symbolic analysis of event-driven web applications

We introduce a trusted infrastructure for the symbolic analysis of modern event-driven Web applica-tions. This infrastructure consists of reference implementations of the DOM Core Level 1, DOM UIEvents, JavaScript Promises and the JavaScriptasync/awaitAPIs, all underpinned by a simpleCore Event Semantics which is sufficiently expressive to describe the event models underlying theseAPIs. Our reference implementations are trustworthy in that three follow the appropriate standardsline-by-line and all are thoroughly tested against the official test-suites, passing all the applicabletests. Using the Core Event Semantics and the reference implementations, we develop JaVerT.Click,a symbolic execution tool for JavaScript that, for the first time, supports reasoning about JavaScriptprograms that use multiple event-related APIs. We demonstrate the viability of JaVerT.Click byproving both the presence and absence of bugs in real-world JavaScript code

Episodic Memory for Cognitive Robots in Dynamic, Unstructured Environments

Elements from cognitive psychology have been applied in a variety of ways to artificial intelligence. One of the lesser studied areas is in how episodic memory can assist learning in cognitive robots. In this dissertation, we investigate how episodic memories can assist a cognitive robot in learning which behaviours are suited to different contexts. We demonstrate the learning system in a domestic robot designed to assist human occupants of a house. People are generally good at anticipating the intentions of others. When around people that we are familiar with, we can predict what they are likely to do next, based on what we have observed them doing before. Our ability to record and recall different types of events that we know are relevant to those types of events is one reason our cognition is so powerful. For a robot to assist rather than hinder a person, artificial agents too require this functionality. This work makes three main contributions. Since episodic memory requires context, we first propose a novel approach to segmenting a metric map into a collection of rooms and corridors. Our approach is based on identifying critical points on a Generalised Voronoi Diagram and creating regions around these critical points. Our results show state of the art accuracy with 98% precision and 96% recall. Our second contribution is our approach to event recall in episodic memory. We take a novel approach in which events in memory are typed and a unique recall policy is learned for each type of event. These policies are learned incrementally, using only information presented to the agent and without any need to take that agent off line. Ripple Down Rules provide a suitable learning mechanism. Our results show that when trained appropriately we achieve a near perfect recall of episodes that match to an observation. Finally we propose a novel approach to how recall policies are trained. Commonly an RDR policy is trained using a human guide where the instructor has the option to discard information that is irrelevant to the situation. However, we show that by using Inductive Logic Programming it is possible to train a recall policy for a given type of event after only a few observations of that type of event

Efficient Information Access in Data-Intensive Sensor Networks

Recent advances in wireless communications and microelectronics have enabled wide deployment of smart sensor networks. Such networks naturally apply to a broad range of applications that involve system monitoring and information tracking (e.g., fine-grained weather/environmental monitoring, structural health monitoring, urban-scale traffic or parking monitoring, gunshot detection, monitoring volcanic eruptions, measuring rate of melting glaciers, forest fire detection, emergency medical care, disaster response, airport security infrastructure, monitoring of children in metropolitan areas, product transition in warehouse networks etc.).Meanwhile, existing wireless sensor networks (WSNs) perform poorly when the applications have high bandwidth needs for data transmission and stringent delay constraints against the network communication. Such requirements are common for Data Intensive Sensor Networks (DISNs) implementing Mission-Critical Monitoring applications (MCM applications).We propose to enhance existing wireless network standards with flexible query optimization strategies that take into account network constraints and application-specific data delivery patterns in order to meet high performance requirements of MCM applications.In this respect, this dissertation has two major contributions: First, we have developed an algebraic framework called Data Transmission Algebra (DTA) for collision-aware concurrent data transmissions. Here, we have merged the serialization concept from the databases with the knowledge of wireless network characteristics. We have developed an optimizer that uses the DTA framework, and generates an optimal data transmission schedule with respect to latency, throughput, and energy usage. We have extended the DTA framework to handle location-based trust and sensor mobility. We improved DTA scalability with Whirlpool data delivery mechanism, which takes advantage of partitioning of the network. Second, we propose relaxed optimization strategy and develop an adaptive approach to deliver data in data-intensive wireless sensor networks. In particular, we have shown that local actions at nodes help network to adapt in worse network conditions and perform better. We show that local decisions at the nodes can converge towards desirable global network properties e.g.,high packet success ratio for the network. We have also developed a network monitoring tool to assess the state and dynamic convergence of the WSN, and force it towards better performance

Coalescent Theory and Yule Trees in time and space

Mathematically, Coalescent Theory describes genealogies within a population in the form of (binary) trees. The original Coalescent Model is based on population models that are evolving neutrally. With respect to graph isomorphy, the tree-structures it provides can be equivalently described in a discrete setting by the Yule Process. As a population evolves (in time), the genealogy of the population is subject to change, and so is the tree structure associated with it. A similar statement holds true if the population is assumed to be recombining; then, in space, i.e. along the genome, the genealogy of a sample may be subject to change in a similar way. The two main focuses of this thesis are the description of the processes that shape the genealogy in time and in space, making use of the relation between Coalescent and Yule Process. As for the process in time, the presented approach differs from existing ones mainly in that the population considered is strictly finite. The results we obtain are of mainly theoretical nature. In case of the process along the genome, we focus on mathematical properties of Linkage Disequilibrium, a quantity that is relevant in the analysis of population-genetical data. Similarities and differences between the two are discussed, and a possibility of performing similar analyses when the assumption of neutrality is abandoned is pointed out