The 1990 progress report and future plans

This document describes the progress and plans of the Artificial Intelligence Research Branch (RIA) at ARC in 1990. Activities span a range from basic scientific research to engineering development and to fielded NASA applications, particularly those applications that are enabled by basic research carried out at RIA. Work is conducted in-house and through collaborative partners in academia and industry. Our major focus is on a limited number of research themes with a dual commitment to technical excellence and proven applicability to NASA short, medium, and long-term problems. RIA acts as the Agency's lead organization for research aspects of artificial intelligence, working closely with a second research laboratory at JPL and AI applications groups at all NASA centers

Automated Specification Inference in a Combined Domain via User-Defined Predicates

Discovering program specifications automatically for heap-manipulating programs is a challenging task due\ud to the complexity of aliasing and mutability of data structures. This task is further complicated by an\ud expressive domain that combines shape, numerical and bag information. In this paper, we propose a compositional analysis framework that would derive the summary for each method in the expressive abstract\ud domain, independently from its callers. We propose a novel abstraction method with a bi-abduction technique in the combined domain to discover pre-/post-conditions that could not be automatically inferred\ud before. The analysis does not only infer memory safety properties, but also finds relationships between pure\ud and shape domains towards full functional correctness of programs. A prototype of the framework has been\ud implemented and initial experiments have shown that our approach can discover interesting properties for\ud non-trivial programs

Verification of Pointer-Based Programs with Partial Information

The proliferation of software across all aspects of people's life means that software failure can bring catastrophic result. It is therefore highly desirable to be able to develop software that is verified to meet its expected specification. This has also been identified as a key objective in one of the UK Grand Challenges (GC6) (Jones et al., 2006; Woodcock, 2006). However, many difficult problems still remain in achieving this objective, partially due to the wide use of (recursive) shared mutable data structures which are hard to keep track of statically in a precise and concise way. This thesis aims at building a verification system for both memory safety and functional correctness of programs manipulating pointer-based data structures, which can deal with two scenarios where only partial information about the program is available. For instance the verifier may be supplied with only partial program specification, or with full specification but only part of the program code. For the first scenario, previous state-of-the-art works (Nguyen et al., 2007; Chin et al., 2007; Nguyen and Chin, 2008; Chin et al, 2010) generally require users to provide full specifications for each method of the program to be verified. Their approach seeks much intellectual effort from users, and meanwhile users are liable to make mistakes in writing such specifications. This thesis proposes a new approach to program verification that allows users to provide only partial specification to methods. Our approach will then refine the given annotation into a more complete specification by discovering missing constraints. The discovered constraints may involve both numerical and multiset properties that could be later confirmed or revised by users. Meanwhile, we further augment our approach by requiring only partial specification to be given for primary methods of a program. Specifications for loops and auxiliary methods can then be systematically discovered by our augmented mechanism, with the help of information propagated from the primary methods. This work is aimed at verifying beyond shape properties, with the eventual goal of analysing both memory safety and functional properties for pointer-based data structures. Initial experiments have confirmed that we can automatically refine partial specifications with non-trivial constraints, thus making it easier for users to handle specifications with richer properties. For the second scenario, many programs contain invocations to unknown components and hence only part of the program code is available to the verifier. As previous works generally require the whole of program code be present, we target at the verification of memory safety and functional correctness of programs manipulating pointer-based data structures, where the program code is only partially available due to invocations to unknown components. Provided with a Hoare-style specification ({Pre} prog {Post}) where program (prog) contains calls to some unknown procedure (unknown), we infer a specification (mspecu) for the unknown part (unknown) from the calling contexts, such that the problem of verifying program (prog) can be safely reduced to the problem of proving that the unknown procedure (unknown) (once its code is available) meets the derived specification (mspecu). The expected specification (mspecu) is automatically calculated using an abduction-based shape analysis specifically designed for a combined abstract domain. We have implemented a system to validate the viability of our approach, with encouraging experimental results

Proceedings of The Multi-Agent Logics, Languages, and Organisations Federated Workshops (MALLOW 2010)

http://ceur-ws.org/Vol-627/allproceedings.pdfInternational audienceMALLOW-2010 is a third edition of a series initiated in 2007 in Durham, and pursued in 2009 in Turin. The objective, as initially stated, is to "provide a venue where: the cost of participation was minimum; participants were able to attend various workshops, so fostering collaboration and cross-fertilization; there was a friendly atmosphere and plenty of time for networking, by maximizing the time participants spent together"

Artificial Intelligence Research Branch future plans

This report contains information on the activities of the Artificial Intelligence Research Branch (FIA) at NASA Ames Research Center (ARC) in 1992, as well as planned work in 1993. These activities span a range from basic scientific research through engineering development to fielded NASA applications, particularly those applications that are enabled by basic research carried out in FIA. Work is conducted in-house and through collaborative partners in academia and industry. All of our work has research themes with a dual commitment to technical excellence and applicability to NASA short, medium, and long-term problems. FIA acts as the Agency's lead organization for research aspects of artificial intelligence, working closely with a second research laboratory at the Jet Propulsion Laboratory (JPL) and AI applications groups throughout all NASA centers. This report is organized along three major research themes: (1) Planning and Scheduling: deciding on a sequence of actions to achieve a set of complex goals and determining when to execute those actions and how to allocate resources to carry them out; (2) Machine Learning: techniques for forming theories about natural and man-made phenomena; and for improving the problem-solving performance of computational systems over time; and (3) Research on the acquisition, representation, and utilization of knowledge in support of diagnosis design of engineered systems and analysis of actual systems

LOGIC AND CONSTRAINT PROGRAMMING FOR COMPUTATIONAL SUSTAINABILITY

Computational Sustainability is an interdisciplinary field that aims to develop computational and mathematical models and methods for decision making concerning the management and allocation of resources in order to help solve environmental problems. This thesis deals with a broad spectrum of such problems (energy efficiency, water management, limiting greenhouse gas emissions and fuel consumption) giving a contribution towards their solution by means of Logic Programming (LP) and Constraint Programming (CP), declarative paradigms from Artificial Intelligence of proven solidity. The problems described in this thesis were proposed by experts of the respective domains and tested on the real data instances they provided. The results are encouraging and show the aptness of the chosen methodologies and approaches. The overall aim of this work is twofold: both to address real world problems in order to achieve practical results and to get, from the application of LP and CP technologies to complex scenarios, feedback and directions useful for their improvement

Qualitative modelling and simulation of physical systems for a diagnostic purpose

This is a Milton Keynes De Montfort University thesisThe goal of a fault-diagnosis system is to obtain an accurate diagnosis at a low cost. In order to reach this goal, many techniques have been used, e.g. qualitative methods and multiple-models. This research investigates a novel strategy for improving the balance accuracy versus cost of consistency-based fault-diagnosis systems. This new strategy is organised around the notion of entities. These are physical entities. such as water pressure or temperature. The functioning of a physical system can involve numerous entities. Because these entities influence each other's behaviour, multiple-fault situations can occur, where several entities are affected by a fault. These situations are called complex multiple-fault situations. The existing fault-diagnosis systems do not perform satisfactorily on complex multiple-fault situations. This is because the set of entities they investigate is fixed from the start of the diagnostic process. As a consequence, depending on the entities included in this set, existing systems either perform an inaccurate diagnosis, or reach an accurate diagnosis at an unnecessarily high cost. This thesis presents a fault-diagnosis strategy called MVDS (standing for Multiple Variable Diagnosis Strategy) designed specifically for performing the efficient diagnosis of complex multiple-fault situations. The underlying principle of MVDS is that it is not possible to know from the start of the diagnostic process which entities are affected. Thus, a diagnostic process with MVDS is undertaken with the investigation of an initial set of entities, and this set of investigated entities is continuously updated along the process, as intermediate results are obtained. In order to illustrate clearly the functioning of MVDS, a fault-scenario using a small example from the air-conditioning domain is diagnosed and the process studied. The investigation of the performance of MVDS on more complex physical systems is undertaken on a larger case-study using a hot-water and heating system. In MVDS, it is possible to disable the adaptability of the set of investigated entities, so that it can be run with a fixed set. By doing so, the performance of the strategy in MVDS can be compared to the performance of traditional approaches which use a fixed set of investigated entities. The study-case shows that MVDS reaches more accurate results than traditional approaches, and that this accuracy is obtained at a low cost, since unnecessary measurements of entities are avoided. Furthermore, the strategy produces a complete trace of the process that is close to common-sense reasoning. It is also a co-operative strategy where the operator can intervene. Summary of the main research contributions: - The issue of diagnosing complex multiple-fault situations is specifically addressed for the first time. The problem caused by this diagnosis task is defined, and a strategy is constructed in order to diagnose efficiently the complex multiple-fault situations. The strategy is implemented in MVDS and tested on an example and a case-study. - Risk characteristics have been described. They allow to evaluate how prone to complex muItiple-fault situations is a physical system. - Hot-water and heating systems are offered as a new domain of research for consistency-based fault-diagnosis systems. - The inclusion of co-operation into the fault-diagnosis process is a novel approach. Its potential advantages have been identified

The Road to General Intelligence

Humans have always dreamed of automating laborious physical and intellectual tasks, but the latter has proved more elusive than naively suspected. Seven decades of systematic study of Artificial Intelligence have witnessed cycles of hubris and despair. The successful realization of General Intelligence (evidenced by the kind of cross-domain flexibility enjoyed by humans) will spawn an industry worth billions and transform the range of viable automation tasks.The recent notable successes of Machine Learning has lead to conjecture that it might be the appropriate technology for delivering General Intelligence. In this book, we argue that the framework of machine learning is fundamentally at odds with any reasonable notion of intelligence and that essential insights from previous decades of AI research are being forgotten. We claim that a fundamental change in perspective is required, mirroring that which took place in the philosophy of science in the mid 20th century. We propose a framework for General Intelligence, together with a reference architecture that emphasizes the need for anytime bounded rationality and a situated denotational semantics. We given necessary emphasis to compositional reasoning, with the required compositionality being provided via principled symbolic-numeric inference mechanisms based on universal constructions from category theory. • Details the pragmatic requirements for real-world General Intelligence. • Describes how machine learning fails to meet these requirements. • Provides a philosophical basis for the proposed approach. • Provides mathematical detail for a reference architecture. • Describes a research program intended to address issues of concern in contemporary AI. The book includes an extensive bibliography, with ~400 entries covering the history of AI and many related areas of computer science and mathematics.The target audience is the entire gamut of Artificial Intelligence/Machine Learning researchers and industrial practitioners. There are a mixture of descriptive and rigorous sections, according to the nature of the topic. Undergraduate mathematics is in general sufficient. Familiarity with category theory is advantageous for a complete understanding of the more advanced sections, but these may be skipped by the reader who desires an overall picture of the essential concepts This is an open access book