Ideas for a high-level proof strategy language

ABSTRACT Finding ways to prove theorems mechanically was one of the earliest challenges tackled by the AI community. Notable progress has been made but there is still always a limit to any set of heuristic search techniques. From a proof done by human users, we wish to find out whether AI techniques can also be used to learn from a human user. AI4FM (Artificial Intelligence for Formal Methods) is a four-year project that starts officially in April 2010 (see www.AI4FM.org). It focuses on helping users of "formal methods" many of which give rise to proof obligations that have to be (mechanically) verified (by a theorem prover). In industrial-sized developments, there are often a large number of proof obligations and, whilst many of them succumb to similar proof strategies, those that remain can hold up engineers trying to use formal methods. The goal of AI4FM is to learn enough from one manual proof, to discharge proof obligations automatically that yield to similar proof strategies. To achieve this, a high-level (proof) strategy language is required, and in this paper we outline some ideas of such language, and towards extracting them. * During this work Gudmund Grov has been employed jointly by University of Edinburgh and Newcastle University. and constrained use of Z [FW08] -is the so-called "posit and prove" approach: a designer posits development steps and then justifies that they satisfy earlier specifications by discharging (often automatically generated) proof obligations (POs). A large proportion of these POs can be discharged by automatic theorem provers but "some" proofs require user interaction. Quantifying "some" is hard since it depends on many factors such as the domain, technology and methodology used -it could be as little as 3% or as much as 40%. For example, the Paris Metro line 14, developed in the Bmethod, generated 27, 800 POs (of which around 2, 250 required user-interaction) [Abr07] -the need for interactive proofs is clearly still a bottleneck in industrial application of FM, notwithstanding high degree of automation. THE FORMAL METHODS PROBLE

Understanding the work and learning of high performance coaches

Background: The development of high performance sports coaches has been proposed as a major imperative in the professionalization of sports coaching. Accordingly, an increasing body of research is beginning to address the question of how coaches learn. While this is important work, an understanding of how coaches learn must be underpinned by an understanding of what coaches do. This is not to suggest a return to the behaviouristic accounts of coaching, rather a greater consideration of what tasks entail modern coaching work, especially within the dynamic and evolving vocation of high performance coaching

System and market failures: the unavailability of magnesium sulphate for the treatment of eclampsia and pre-eclampsia in Mozambique and Zimbabwe.

Low cost and effective drugs, such as magnesium sulphate, need to be included in initiatives to improve access to essential medicines in Afric

Knowledge-based vision and simple visual machines

The vast majority of work in machine vision emphasizes the representation of perceived objects and events: it is these internal representations that incorporate the 'knowledge' in knowledge-based vision or form the 'models' in model-based vision. In this paper, we discuss simple machine vision systems developed by artificial evolution rather than traditional engineering design techniques, and note that the task of identifying internal representations within such systems is made difficult by the lack of an operational definition of representation at the causal mechanistic level. Consequently, we question the nature and indeed the existence of representations posited to be used within natural vision systems (i.e. animals). We conclude that representations argued for on a priori grounds by external observers of a particular vision system may well be illusory, and are at best place-holders for yet-to-be-identified causal mechanistic interactions. That is, applying the knowledge-based vision approach in the understanding of evolved systems (machines or animals) may well lead to theories and models that are internally consistent, computationally plausible, and entirely wrong

Modeling and Simulation of the Second-Generation Orion Crew Module Air Bag Landing System

Air bags were evaluated as the landing attenuation system for earth landing of the Orion Crew Module (CM). Analysis conducted to date shows that airbags are capable of providing a graceful landing of the CM in nominal and off-nominal conditions such as parachute failure, high horizontal winds, and unfavorable vehicle/ground angle combinations, while meeting crew and vehicle safety requirements. The analyses and associated testing presented here surround a second generation of the airbag design developed by ILC Dover, building off of relevant first-generation design, analysis, and testing efforts. In order to fully evaluate the second generation air bag design and correlate the dynamic simulations, a series of drop tests were carried out at NASA Langley s Landing and Impact Research (LandIR) facility in Hampton, Virginia. The tests consisted of a full-scale set of air bags attached to a full-scale test article representing the Orion Crew Module. The techniques used to collect experimental data, develop the simulations, and make comparisons to experimental data are discussed

Cultivating Social Resources on Social Network Sites: Facebook Relationship Maintenance Behaviors and Their Role in Social Capital Processes

This study explores the relationship between perceived bridging social capital and specific Facebook‐enabled communication behaviors using survey data from a sample of U.S. adults (N=614). We explore the role of a specific set of Facebook behaviors that support relationship maintenance and assess the extent to which demographic variables, time on site, total and “actual” Facebook Friends, and this new measure (Facebook Relationship Maintenance Behaviors) predict bridging social capital. Drawing upon scholarship on social capital and relationship maintenance, we discuss the role of social grooming and attention‐signaling activities in shaping perceived access to resources in one's network as measured by bridging social capital.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/108031/1/jcc412078.pd