A Formal Framework for Speedup Learning from Problems and Solutions

Speedup learning seeks to improve the computational efficiency of problem solving with experience. In this paper, we develop a formal framework for learning efficient problem solving from random problems and their solutions. We apply this framework to two different representations of learned knowledge, namely control rules and macro-operators, and prove theorems that identify sufficient conditions for learning in each representation. Our proofs are constructive in that they are accompanied with learning algorithms. Our framework captures both empirical and explanation-based speedup learning in a unified fashion. We illustrate our framework with implementations in two domains: symbolic integration and Eight Puzzle. This work integrates many strands of experimental and theoretical work in machine learning, including empirical learning of control rules, macro-operator learning, Explanation-Based Learning (EBL), and Probably Approximately Correct (PAC) Learning.Comment: See http://www.jair.org/ for any accompanying file

A Selective Macro-learning Algorithm and its Application to the NxN Sliding-Tile Puzzle

One of the most common mechanisms used for speeding up problem solvers is macro-learning. Macros are sequences of basic operators acquired during problem solving. Macros are used by the problem solver as if they were basic operators. The major problem that macro-learning presents is the vast number of macros that are available for acquisition. Macros increase the branching factor of the search space and can severely degrade problem-solving efficiency. To make macro learning useful, a program must be selective in acquiring and utilizing macros. This paper describes a general method for selective acquisition of macros. Solvable training problems are generated in increasing order of difficulty. The only macros acquired are those that take the problem solver out of a local minimum to a better state. The utility of the method is demonstrated in several domains, including the domain of NxN sliding-tile puzzles. After learning on small puzzles, the system is able to efficiently solve puzzles of any size.Comment: See http://www.jair.org/ for an online appendix and other files accompanying this articl

Working Notes from the 1992 AAAI Spring Symposium on Practical Approaches to Scheduling and Planning

The symposium presented issues involved in the development of scheduling systems that can deal with resource and time limitations. To qualify, a system must be implemented and tested to some degree on non-trivial problems (ideally, on real-world problems). However, a system need not be fully deployed to qualify. Systems that schedule actions in terms of metric time constraints typically represent and reason about an external numeric clock or calendar and can be contrasted with those systems that represent time purely symbolically. The following topics are discussed: integrating planning and scheduling; integrating symbolic goals and numerical utilities; managing uncertainty; incremental rescheduling; managing limited computation time; anytime scheduling and planning algorithms, systems; dependency analysis and schedule reuse; management of schedule and plan execution; and incorporation of discrete event techniques

Learning in behavioural robotics

The research described in this thesis examines how machine learning mechanisms can be used in an assembly robot system to improve the reliability of the system and reduce the development workload, without reducing the flexibility of the system. The justification foi' this is that for a robot to be performing effectively it is frequently necessary to have gained experience of its performance under a particular configuration before that configuration can be altered to produce a performance improvement. Machine learning mechanisms can automate this activity of testing, evaluating and then changing.From studying how other researchers have developed working robot systems the activities which require most effort and experimentation are:-• The selection of the optimal parameter settings. • The establishment of the action-sensor couplings which are necessary for the effective handling of uncertainty. • Choosing which way to achieve a goal.One way to implement the first two kinds of learning is to specify a model of the coupling or the interaction of parameters and results, and from that model derive an appropriate learning mechanism that will find a parametrisation for that model that will enable good performance to be obtained. From this starting point it has been possible to show how equal, or better performance can be obtained by using iearning mechanisms which are neither derived from nor require a model of the task being learned. Instead, by combining iteration and a task specific profit function it is possible to use a generic behavioural module based on a learning mechanism to achieve the task.Iteration and a task specific profit function can also be used to learn which behavioural module from a pool of equally competent modules is the best at any one time to use to achieve a particular goal. Like the other two kinds of learning, this successfully automates an otherwise difficult test and evaluation process that would have to be performed by a developer. In doing so effectively, it, like the other learning that has been used here, shows that instead of being a peripheral issue to be introduced to a working system, learning, carried out in the right way, can be instrumental in the production of that working system

Simple low cost causal discovery using mutual information and domain knowledge

PhDThis thesis examines causal discovery within datasets, in particular observational datasets where normal experimental manipulation is not possible. A number of machine learning techniques are examined in relation to their use of knowledge and the insights they can provide regarding the situation under study. Their use of prior knowledge and the causal knowledge produced by the learners are examined. Current causal learning algorithms are discussed in terms of their strengths and limitations. The main contribution of the thesis is a new causal learner LUMIN that operates with a polynomial time complexity in both the number of variables and records examined. It makes no prior assumptions about the form of the relationships and is capable of making extensive use of available domain information. This learner is compared to a number of current learning algorithms and it is shown to be competitive with them

Enabling peer-to-peer remote experimentation in distributed online remote laboratories

Remote Access Laboratories (RALs) are online platforms that allow human user interaction with physical instruments over the Internet. Usually RALs follow a client-server paradigm. Dedicated providers create and maintain experiments and corresponding educational content. In contrast, this dissertation focuses on a Peer-to-Peer (P2P) service model for RALs where users are encouraged to host experiments at their location. This approach can be seen as an example of an Internet of Things (IoT) system. A set of smart devices work together providing a cyber-physical interface for users to run experiments remotely via the Internet. The majority of traditional RAL learning activities focus on undergraduate education where hands-on experience such as building experiments, is not a major focus. In contrast this work is motivated by the need to improve Science, Technology, Engineering and Mathematics (STEM) education for school-aged children. Here physically constructing experiments forms a substantial part of the learning experience. In the proposed approach, experiments can be designed with relatively simple components such as LEGO Mindstorms or Arduinos. The user interface can be programed using SNAP!, a graphical programming tool. While the motivation for the work is educational in nature, this thesis focuses on the technical details of experiment control in an opportunistic distributed environment. P2P RAL aims to enable any two random participants in the system - one in the role of maker creating and hosting an experiment and one in the role of learner using the experiment - to establish a communication session during which the learner runs the remote experiment through the Internet without requiring a centralized experiment or service provider. The makers need to have support to create the experiment according to a common web based programing interface. Thus, the P2P approach of RALs requires an architecture that provides a set of heterogeneous tools which can be used by makers to create a wide variety of experiments. The core contribution of this dissertation is an automaton-based model (twin finite state automata) of the controller units and the controller interface of an experiment. This enables the creation of experiments based on a common platform, both in terms of software and hardware. This architecture enables further development of algorithms for evaluating and supporting the performance of users which is demonstrated through a number of algorithms. It can also ensure the safety of instruments with intelligent tools. The proposed network architecture for P2P RALs is designed to minimise latency to improve user satisfaction and learning experience. As experiment availability is limited for this approach of RALs, novel scheduling strategies are proposed. Each of these contributions has been validated through either simulations, e.g. in case of network architecture and scheduling, or test-bed implementations, in case of the intelligent tools. Three example experiments are discussed along with users' feedback on their experience of creating an experiment and using others’ experimental setup. The focus of the thesis is mainly on the design and hosting of experiments and ensuring user accessibility to them. The main contributions of this thesis are in regards to machine learning and data mining techniques applied to IoT systems in order to realize the P2P RALs system. This research has shown that a P2P architecture of RALs can provide a wide variety of experimental setups in a modular environment with high scalability. It can potentially enhance the user-learning experience while aiding the makers of experiments. It presents new aspects of learning analytics mechanisms to monitor and support users while running experiments, thus lending itself to further research. The proposed mathematical models are also applicable to other Internet of Things applications

Secure and Privacy-Preserving Vehicular Communications

Road safety has been drawing increasing attention in the public, and has been subject to extensive efforts from both industry and academia in mitigating the impact of traffic accidents. Recent advances in wireless technology promise new approaches to facilitating road safety and traffic management, where each vehicle (or referred to as On-board unit (OBU)) is allowed to communicate with each other as well as with Roadside units (RSUs), which are located in some critical sections of the road, such as a traffic light, an intersection, and a stop sign. With the OBUs and RSUs, a self-organized network, called Vehicular Ad Hoc Network (VANET), can thus be formed. Unfortunately, VANETs have faced various security threats and privacy concerns, which would jeopardize the public safety and become the main barrier to the acceptance of such a new technology. Hence, addressing security and privacy issues is a prerequisite for a market-ready VANET. Although many studies have recently addressed a significant amount of efforts in solving the related problems, few of the studies has taken the scalability issues into consideration. When the traffic density is getting large, a vehicle may become unable to verify the authenticity of the messages sent by its neighbors in a timely manner, which may result in message loss so that public safety may be at risk. Communication overhead is another issue that has not been well addressed in previously reported studies. Many efforts have been made in recent years in achieving efficient broadcast source authentication and data integrity by using fast symmetric cryptography. However, the dynamic nature of VANETs makes it very challenging in the applicability of these symmetric cryptography-based protocols. In this research, we propose a novel Secure and Efficient RSU-aided Privacy Preservation Protocol, called SERP^3, in order to achieve efficient secure and privacy-preserving Inter-Vehicle Communications (IVCs). With the commitments of one-way key chains distributed to vehicles by RSUs, a vehicle can effectively authenticate any received message from vehicles nearby even in the presence of frequent change of its neighborship. Compared with previously reported public key infrastructure (PKI)-based packet authentication protocols for security and privacy, the proposed protocol not only retains the security and privacy preservation properties, but also has less packet loss ratio and lower communication overhead, especially when the road traffic is heavy. Therefore, the protocol solves the scalability and communication overhead issues, while maintaining acceptable packet latency. However, RSU may not exist in some situations, for example, in the early stage deployment phase of VANET, where unfortunately, SERP^3 is not suitable. Thus, we propose a complementary Efficient and Cooperative Message Validation Protocol, called ECMVP, where each vehicle probabilistically validates a certain percentage of its received messages based on its own computing capacity and then reports any invalid messages detected by it. Since the ultimate goal of designing VANET is to develop vehicle safety/non-safety related applications to improve road safety and facilitate traffic management, two vehicle applications are further proposed in the research to exploit the advantages of vehicular communications. First, a novel vehicle safety application for achieving a secure road traffic control system in VANETs is developed. The proposed application helps circumvent vehicles safely and securely through the areas in any abnormal situation, such as a car crash scene, while ensuring the security and privacy of the drivers from various threats. It not only enhances traveler safety but also minimizes capacity restrictions due to any unusual situation. Second, the dissertation investigates a novel mobile payment system for highway toll collection by way of vehicular communications, which addresses all the issues in the currently existing toll collection technologies