The next-generation Airborne Collision Avoidance System (ACAS X) is intended to be installed on all large aircraft to give advice to pilots and prevent mid-air collisions with other aircraft. It is currently being developed by the Federal Aviation Administration (FAA). In this paper we determine the geometric configurations under which the advice given by ACAS X is safe under a precise set of assumptions and formally verify these configurations using hybrid systems theorem proving techniques. We conduct an initial examination of the current version of the real ACAS X system and discuss some cases where our safety theorem conflicts with the actual advisory given by that version, demonstrating how formal, hybrid approaches are helping ensure the safety of ACAS X. Our approach is general and could also be used to identify unsafe advice issued by other collision avoidance systems or confirm their safety.
Formal verification of industrial systems is very challenging, due to reasons ranging from scalability issues to communication difficulties with engineering-focused teams. More importantly, industrial systems are rarely designed for verification, but rather for operational needs. In this paper we present an overview of our experience using hybrid systems theorem proving to formally verify ACAS X, an airborne collision avoidance system for airliners scheduled to be operational around 2020. The methods and proof techniques presented here are an overview of the work already presented in [8], while the evaluation of ACAS X has been significantly expanded and updated to the most recent version of the system, run 13. The effort presented in this paper is an integral part of the ACAS X development and was performed in tight collaboration with the ACAS X development team.
Complex software systems are becoming increasingly prevalent in aerospace applications, in particular to accomplish critical tasks. Ensuring the safety of these systems is crucial, while they can have subtly different behavior under slight variations in operating conditions. In this paper we advocate the use of formal verification techniques and in particular theorem proving for hybrid software-intensive systems as a wellfounded complementary approach to the classical aerospace verification and validation techniques such as testing or simulation. As an illustration of these techniques, we study a novel lateral mid-air collision avoidance maneuver in an ideal setting, without accounting for the uncertainties of the physical reality. We then detail the challenges that naturally arise when applying such technology to industrial-scale applications and our proposals on how to address these issues.
There exist many algorithms for learning how to play repeated bimatrix games. Most of these algorithms are justified in terms of some sort of theoretical guarantee. On the other hand, little is known about the empirical performance of these algorithms. Most such claims in the literature are based on small experiments, which has hampered understanding as well as the development of new multiagent learning (MAL) algorithms. We have developed a new suite of tools for running multiagent experiments: the MultiAgent Learning Testbed (MALT). These tools are designed to facilitate larger and more comprehensive experiments by removing the need to build one-off experimental code. MALT also provides baseline implementations of many MAL algorithms, hopefully eliminating or reducing differences between algorithm implementations and increasing the reproducibility of results. Using this test suite, we ran an experiment unprecedented in size. We analyzed the results according to a variety of performance metrics including reward, maxmin distance, regret, and several notions of equilibrium convergence. We confirmed several pieces of conventional wisdom, but also discovered some surprising results. For example, we found that single-agent Q-learning outperformed many more complicated and more modern MAL algorithms.
Memory is rapidly becoming a precious resource in many data processing environments. This paper introduces a new data structure called a Compressed Buffer Tree (CBT). Using a combination of buffering, compression, and lazy aggregation, CBTs can improve the memory efficiency of the GroupBy-Aggregate abstraction which forms the basis of many data processing models like MapReduce and databases. We evaluate CBTs in the context of MapReduce aggregation, and show that CBTs can provide significant advantages over existing hashbased aggregation techniques: up to 2× less memory and 1.5× the throughput, at the cost of 2.5× CPU.
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