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

Inherently flexible software

Software evolution is an important and expensive consequence of software. As Lehman's First Law of Program Evolution states, software must be changed to satisfy new user requirements or become progressively less useful to the stakeholders of the software. Software evolution is difficult for a multitude of different reasons, most notably because of an inherent lack of evolveability of software, design decisions and existing requirements which are difficult to change and conflicts between new requirements and existing assumptions and requirements. Software engineering has traditionally focussed on improvements in software development techniques, with little conscious regard for their effects on software evolution. The thesis emphasises design for change, a philosophy that stems from ideas in preventive maintenance and places the ease of software evolution more at the centre of the design of software systems than it is at present. The approach involves exploring issues of evolveability, such as adaptability, flexibility and extensibility with respect to existing software languages, models and architectures. A software model, SEvEn, is proposed which improves on the evolveability of these existing software models by improving on their adaptability, flexibility and extensibility, and provides a way to determine the ripple effects of changes by providing a reflective model of a software system. The main conclusion is that, whilst software evolveability can be improved, complete adaptability, flexibility and extensibility of a software system is not possible, hi addition, ripple effects can't be completely eradicated because assumptions will always persist in a software system and new requirements may conflict with existing requirements. However, the proposed reflective model of software (which consists of a set of software entities, or abstractions, with the characteristic of increased evolveability) provides trace-ability of ripple effects because it explicitly models the dependencies that exist between software entities, determines how software entities can change, ascertains the adaptability of software entities to changes in other software entities on which they depend and determines how changes to software entities affect those software entities that depend on them

A Map-Reduce Parallel Approach to Automatic Synthesis of Control Software

Many Control Systems are indeed Software Based Control Systems, i.e. control systems whose controller consists of control software running on a microcontroller device. This motivates investigation on Formal Model Based Design approaches for automatic synthesis of control software. Available algorithms and tools (e.g., QKS) may require weeks or even months of computation to synthesize control software for large-size systems. This motivates search for parallel algorithms for control software synthesis. In this paper, we present a Map-Reduce style parallel algorithm for control software synthesis when the controlled system (plant) is modeled as discrete time linear hybrid system. Furthermore we present an MPI-based implementation PQKS of our algorithm. To the best of our knowledge, this is the first parallel approach for control software synthesis. We experimentally show effectiveness of PQKS on two classical control synthesis problems: the inverted pendulum and the multi-input buck DC/DC converter. Experiments show that PQKS efficiency is above 65%. As an example, PQKS requires about 16 hours to complete the synthesis of control software for the pendulum on a cluster with 60 processors, instead of the 25 days needed by the sequential algorithm in QKS.Comment: To be submitted to TACAS 2013. arXiv admin note: substantial text overlap with arXiv:1207.4474, arXiv:1207.409

Extending Traditional Static Analysis Techniques to Support Development, Testing and Maintenance of Component-Based Solutions

Traditional static code analysis encompasses a mature set of techniques for helping understand and optimize programs, such as dead code elimination, program slicing, and partial evaluation (code specialization). It is well understood that compared to other program analysis techniques (e.g., dynamic analysis), static analysis techniques do a reasonable job for the cost associated with implementing them. Industry and government are moving away from more ‘traditional’ development approaches towards component-based approaches as ‘the norm.’ Component-based applications most often comprise a collection of distributed object-oriented components such as forms, code snippets, reports, modules, databases, objects, containers, and the like. These components are glued together by code typically written in a visual language. Some industrial experience shows that component-based development and the subsequent use of visual development environments, while reducing an application\u27s total development time, actually increase certain maintenance problems. This provides a motivation for using automated analysis techniques on such systems. The results of this research show that traditional static analysis techniques may not be sufficient for analyzing component-based systems. We examine closely the characteristics of a component-based system and document many of the issues that we feel make the development, analysis, testing and maintenance of such systems more difficult. By analyzing additional summary information for the components as well as any available source code for an application, we show ways in which traditional static analysis techniques may be augmented, thereby increasing the accuracy of static analysis results and ultimately making the maintenance of component-based systems a manageable task. We develop a technique to use semantic information about component properties obtained from type library and interface definition language files, and demonstrate this technique by extending a traditional unreachable code algorithm. To support more complex analysis, we then develop a technique for component developers to provide summary information about a component. This information can be integrated with several traditional static analysis techniques to analyze component-based systems more precisely. We then demonstrate the effectiveness of these techniques on several real Department of Defense (DoD) COTS component-based systems

Supporting the grow-and-prune model for evolving software product lines

207 p.Software Product Lines (SPLs) aim at supporting the development of a whole family of software products through a systematic reuse of shared assets. To this end, SPL development is separated into two interrelated processes: (1) domain engineering (DE), where the scope and variability of the system is defined and reusable core-assets are developed; and (2) application engineering (AE), where products are derived by selecting core assets and resolving variability. Evolution in SPLs is considered to be more challenging than in traditional systems, as both core-assets and products need to co-evolve. The so-called grow-and-prune model has proven great flexibility to incrementally evolve an SPL by letting the products grow, and later prune the product functionalities deemed useful by refactoring and merging them back to the reusable SPL core-asset base. This Thesis aims at supporting the grow-and-prune model as for initiating and enacting the pruning. Initiating the pruning requires SPL engineers to conduct customization analysis, i.e. analyzing how products have changed the core-assets. Customization analysis aims at identifying interesting product customizations to be ported to the core-asset base. However, existing tools do not fulfill engineers needs to conduct this practice. To address this issue, this Thesis elaborates on the SPL engineers' needs when conducting customization analysis, and proposes a data-warehouse approach to help SPL engineers on the analysis. Once the interesting customizations have been identified, the pruning needs to be enacted. This means that product code needs to be ported to the core-asset realm, while products are upgraded with newer functionalities and bug-fixes available in newer core-asset releases. Herein, synchronizing both parties through sync paths is required. However, the state of-the-art tools are not tailored to SPL sync paths, and this hinders synchronizing core-assets and products. To address this issue, this Thesis proposes to leverage existing Version Control Systems (i.e. git/Github) to provide sync operations as first-class construct