We create a unified framework for analyzing and synthesizing plans with loops for solving problems with non-deterministic numeric effects and a limited form of partial observability. Three different action models---with deterministic, qualitative non-deterministic and Boolean non-deterministic semantics---are handled using a single abstract representation. We establish the conditions under which the correctness and termination of solutions, represented as abstract policies, can be verified. We also examine the feasibility of learning abstract policies from examples. We demonstrate our techniques on several planning problems and show that they apply to challenging real-world tasks such as doing the laundry with a PR2 robot. These results resolve a number of open questions about planning with loops and facilitate the development of new algorithms and applications.

Optimising black-box functions is important in many disciplines, such as tuning machine learning models, robotics, finance and mining exploration. Bayesian optimisation is a state-of-the-art technique for the global optimisation of black-box functions which are expensive to evaluate. At the core of this approach is a Gaussian process prior that captures our belief about the distribution over functions. However, in many cases a single Gaussian process is not flexible enough to capture non-stationarity in the objective function. Consequently, heteroscedasticity negatively affects performance of traditional Bayesian methods. In this paper, we propose a novel prior model with hierarchical parameter learning that tackles the problem of non-stationarity in Bayesian optimisation. Our results demonstrate substantial improvements in a wide range of applications, including automatic machine learning and mining exploration.

Since semantic trajectories can discover more semantic meanings of a user’s interests without geographic restrictions, research on semantic trajectories has attracted a lot of attentions in recent years. Most existing work discover the similar behavior of moving objects through analysis of their semantic trajectory pattern, that is, sequences of locations. However, this kind of trajectories without considering the duration of staying on a location limits wild applications. For example, Tom and Anne have a common pattern of Home Restaurant Company Restaurant , but they are not similar, since Tom works at Restaurant , sends snack to someone at Company and return to Restaurant while Anne has breakfast at Restaurant , works at Company and has lunch at Restaurant . If we consider duration of staying on each location we can easily to differentiate their behaviors. In this paper, we propose a novel approach for discovering common behaviors by considering the duration of staying on each location of trajectories (DoSTra). Our approach can be used to detect the group that has similar lifestyle, habit or behavior patterns and predict the future locations of moving objects. We evaluate the experiment based on synthetic dataset, which demonstrates the high effectiveness and efficiency of the proposed method.

Residential and commercial buildings are responsible for about 40% of primary energy consumption in the United States, hence improving their energy efficiency could have important sustainability benefits. The design of a building has tremendous effect on its energy profile, and recently there has been an increased interest in developing optimization methods that support the design of high performance buildings. Previous approaches are either based on simulation optimization or on training an accurate predictive model that is queried during the optimization. We propose a method that more tightly integrates the machine learning and optimization components, by employing active learning during optimization. In particular, we use a Gaussian Process (GP) model for the prediction and active learning and multi-objective genetic algorithm NSGA-II for the optimization. We develop a comprehensive and publicly available benchmark for building design optimization. We evaluate 5 machine learning approaches on our dataset, and show that the GP model is competitive, in addition to being well-suited for the active learning setting. We compare our optimization approach against the 2-stage approach and simulation optimization. Our results show that our approach produces solutions at the Pareto frontier compared to the other two approaches, while using only a fraction of the simulations and time.

The goal of constraint-based sequence mining is to find sequences of symbols that are included in a large number of input sequences and that satisfy some constraints specified by the user. Many constraints have been proposed in the literature, but a general framework is still missing. We investigate the use of constraint programming as general framework for this task. We first identify four categories of constraints that are applicable to sequence mining. We then propose two constraint programming formulations. The first formulation introduces a new global constraint called exists-embedding. This formulation is the most efficient but does not support one type of constraint. To support such constraints, we develop a second formulation that is more general but incurs more overhead. Both formulations can use the projected database technique used in specialised algorithms. Experiments demonstrate the flexibility towards constraint-based settings and compare the approach to existing methods.

We present a novel algorithm, Westfall-Young light, for detecting patterns, such as itemsets and subgraphs, which are statistically significantly enriched in one of two classes. Our method corrects rigorously for multiple hypothesis testing and correlations between patterns through the Westfall-Young permutation procedure, which empirically estimates the null distribution of pattern frequencies in each class via permutations. In our experiments, Westfall-Young light dramatically outperforms the current state-of-the-art approach in terms of both runtime and memory efficiency on popular real-world benchmark datasets for pattern mining. The key to this efficiency is that unlike all existing methods, our algorithm neither needs to solve the underlying frequent itemset mining problem anew for each permutation nor needs to store the occurrence list of all frequent patterns. Westfall-Young light opens the door to significant pattern mining on large datasets that previously led to prohibitive runtime or memory costs.

ABSTRACT Prospector is a computer consultant system intended to aid geologists in evaluating the favorability of an exploration site or region for occurrences of ore deposits of particular types. Knowledge about a particular type of ore deposit is encoded in a computational model representing observable geological features and the relative significance thereof. We describe the form of models in Prospector, focussing on inference networks of geological assertions and the Bayesian propagation formalism used to represent the judgmental reasoning process of the economic geologist who serves as model designer. Following the initial design of a model, simple performance evaluation techniques are used to assess the extent to which the performance of the model reflects faithfully the intent of the model designer. These results identify specific portions of the model that might benefit from "fine tuning", and establish priorities for such revisions. This description of the Prospector system and the model design process serves to illustrate the process of transferring human expertise about a subjective domain into a mechanical realization. I. INTRODUCTION In an increasingly complex and specialized world, human expertise about diverse subjects spanning scientific, economic, social, and political issues plays an increasingly important role in the functioning of all kinds of organizations. Although computers have become indispensable tools in many endeavors, we continue to rely heavily on the human expert's ability to identify and synthesize diverse factors, to form judgments, evaluate alternatives, and make decisions -- in sum, to apply his or her years of experience to the problem at hand. This is especially valid with regard to domains that are not easily amenable to precise scientific formulations, i.e., to domains in which experience and subjective judgment plays a major role.

Another concern has been to exploit (d) detection of metabolic disorders of genetic, developmental, toxic or infectious the AI methodology to understand better some fundamental questions in the origins by identification of organic constituents excreted in abnormal quantities philosophy of science, for example the processes by which explanatory hypotheses in human body fluids.

B.RANDELL 3 PROGRAM PROOF AND MANIPULATION 2 Some techniques for proving correctness of programs which alter data structures.

The two outstanding figures in the history of computer science are Alan Turing and John von Neumann, and they shared the view that logic was the key to understanding and automating computation. In particular, it was Turing who gave us in the mid-1930s the fundamental analysis, and the logical definition, of the concept of'computability by machine' and who discovered the surprising and beautiful basic fact that there exist universal machines which by suitable programming can be made to t This essay is an expanded and revised version of one entitled The Role of Logic in Computer Science and Artificial Intelligence, which was completed in January 1992 (and was later published in the Proceedings of the Fifth Generation computer Systems 1992 Conference). Since completing that essay I have had the benefit of extremely helpful discussions on many of the details with Professor Donald Michie and Professor I. J. Good, both of whom knew Turing well during the war years at Bletchley Park. Professor J. A. N. Lee, whose knowledge of the literature and archives of the history of computing is encyclopedic, also provided additional information, some of which is still unpublished. Further light has very recently been shed on the von Neumann side of the story by Norman Macrae's excellent biography John von Neumann (Macrae 1992). Accordingly, it seemed appropriate to undertake a more complete and thorough version of the FGCS'92 essay, focussing somewhat more on the interesting historical and biographical issues. I am grateful to Donald Michie and Stephen Muggleton for inviting me to contribute such a'second edition' to the present volume, and I would also like to thank the Institute for New Computer Technology (ICOT) for kind permission to make use of the FGCS'92 essay in this way. 1 LOGIC, COMPUTERS, TURING, AND VON NEUMANN