scholarly journals On Testing of Uniform Samplers

2019 ◽  
Vol 33 ◽  
pp. 7777-7784 ◽  
Author(s):  
Sourav Chakraborty ◽  
Kuldeep S. Meel
Keyword(s):  
Artificial Intelligence ◽  
Software Engineering ◽  
Uniform Distribution ◽  
Automobile Industry ◽  
Probabilistic Reasoning ◽  
State Of The Art ◽  
Affirmative Answer ◽  
Sampling Techniques ◽  
Program Testing ◽  
Algorithmic Framework

Recent years have seen an unprecedented adoption of artificial intelligence in a wide variety of applications ranging from medical diagnosis, automobile industry, security to aircraft collision avoidance. Probabilistic reasoning is a key component of such modern artificial intelligence systems. Sampling techniques form the core of the state of the art probabilistic reasoning systems. The divide between the existence of sampling techniques that have strong theoretical guarantees but fail to scale and scalable techniques with weak or no theoretical guarantees mirrors the gap in software engineering between poor scalability of classical program synthesis techniques and billions of programs that are routinely used by practitioners. One bridge connecting the two extremes in the context of software engineering has been program testing. In contrast to testing for deterministic programs, where one trace is sufficient to prove the existence of a bug, in case of samplers one sample is typically not sufficient to prove non-conformity of the sampler to the desired distribution. This makes one wonder whether it is possible to design testing methodology to test whether a sampler under test generates samples close to a given distribution. The primary contribution of this paper is an affirmative answer to the above question when the given distribution is a uniform distribution: We design, to the best of our knowledge, the first algorithmic framework, Barbarik, to test whether the distribution generated is ε−close or η−far from the uniform distribution. In contrast to the sampling techniques that require an exponential or sub-exponential number of samples for sampler whose support can be represented by n bits, Barbarik requires only O(1/(η−ε)4) samples. We present a prototype implementation of Barbarik and use it to test three state of the art uniform samplers over the support defined by combinatorial constraints. Barbarik can provide a certificate of uniformity to one sampler and demonstrate nonuniformity for the other two samplers.

scholarly journals Making High-Performance Robots Safe and Easy to Use For an Introduction to Computing

2020 ◽  
Vol 34 (09) ◽  
pp. 13412-13419
Author(s):  
Joseph Spitzer ◽  
Joydeep Biswas ◽  
Arjun Guha
Keyword(s):  
Artificial Intelligence ◽  
Software Engineering ◽  
Empirical Evidence ◽  
High Performance ◽  
State Of The Art ◽  
Levels Of Abstraction

Robots are a popular platform for introducing computing and artificial intelligence to novice programmers. However, programming state-of-the-art robots is very challenging, and requires knowledge of concurrency, operation safety, and software engineering skills, which can take years to teach. In this paper, we present an approach to introducing computing that allows students to safely and easily program high-performance robots. We develop a platform for students to program RoboCup Small Size League robots using JavaScript. The platform 1) ensures physical safety at several levels of abstraction, 2) allows students to program robots using JavaScript in the browser, without the need to install software, and 3) presents a simplified JavaScript semantics that shields students from confusing language features. We discuss our experience running a week-long workshop using this platform, and analyze over 3,000 student-written program revisions to provide empirical evidence that our approach does help students.

Report from the 2nd Int. Workshop on Education through Advanced Software Engineering and Artificial Intelligence (EASEAI '20)

2021 ◽  
Vol 46 (2) ◽  
pp. 28-29
Author(s):  
Benoît Vanderose ◽  
Julie Henry ◽  
Benoît Frénay ◽  
Xavier Devroey
Keyword(s):  
Artificial Intelligence ◽  
Software Engineering ◽  
Engineering Education ◽  
State Of The Art ◽  
Future Directions ◽  
The Past ◽  
Software Engineering Education ◽  
Current State ◽  
Amount Of Knowledge ◽  
Software Engineering Tools

In the past years, with the development and widespread of digi- tal technologies, everyday life has been profoundly transformed. The general public, as well as specialized audiences, have to face an ever-increasing amount of knowledge and learn new abilities. The EASEAI workshop series addresses that challenge by look- ing at software engineering, education, and arti cial intelligence research elds to explore how they can be combined. Speci cally, this workshop brings together researchers, teachers, and practi- tioners who use advanced software engineering tools and arti cial intelligence techniques in the education eld and through a trans- generational and transdisciplinary range of students to discuss the current state of the art and practices, and establish new future directions. More information at https://easeai.github.io.

The 8th International Workshop on Realizing Artificial Intelligence Synergies in Software Engineering

2021 ◽  
Vol 46 (1) ◽  
pp. 23-24
Author(s):  
Shin Yoo ◽  
Aldeida Aleti ◽  
Burak Turhan ◽  
Leandro L. Minku ◽  
Andriy Miranskyy ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Software Engineering ◽  
Distribution Systems ◽  
State Of The Art ◽  
International Workshop ◽  
Transport Systems ◽  
Medical Applications ◽  
Safety Critical ◽  
Software Production ◽  
Development Costs

The International Workshop on Realizing Arti cial Intelligence Synergies in Software Engineering (RAISE) aims to present the state of the art in the crossover between Software Engineering and Arti cial Intelligence. This workshop explored not only the appli- cation of AI techniques to SE problems but also the application of SE techniques to AI problems. Software has become critical for realizing functions central to our society. For example, software is essential for nancial and transport systems, energy generation and distribution systems, and safety-critical medical applications. Software development costs trillions of dollars each year yet, still, many of our software engineering methods remain mostly man- ual. If we can improve software production by smarter AI-based methods, even by small margins, then this would improve a crit- ical component of the international infrastructure, while freeing up tens of billions of dollars for other tasks.

scholarly journals BIRD: Engineering an Efficient CNF-XOR SAT Solver and Its Applications to Approximate Model Counting

2019 ◽  
Vol 33 ◽  
pp. 1592-1599 ◽  
Author(s):  
Mate Soos ◽  
Kuldeep S. Meel
Keyword(s):  
Probabilistic Reasoning ◽  
State Of The Art ◽  
Fundamental Problem ◽  
Approximate Model ◽  
Affirmative Answer ◽  
The State ◽  
Boolean Formula ◽  
Wide Range ◽  
Model Counting ◽  
Sat Solver

Given a Boolean formula φ, the problem of model counting, also referred to as #SAT is to compute the number of solutions of φ. Model counting is a fundamental problem in artificial intelligence with a wide range of applications including probabilistic reasoning, decision making under uncertainty, quantified information flow, and the like. Motivated by the success of SAT solvers, there has been surge of interest in the design of hashing-based techniques for approximate model counting for the past decade. We profiled the state of the art approximate model counter ApproxMC2 and observed that over 99.99% of time is consumed by the underlying SAT solver, CryptoMiniSat. This observation motivated us to ask: Can we design an efficient underlying CNF-XOR SAT solver that can take advantage of the structure of hashing-based algorithms and would this lead to an efficient approximate model counter? The primary contribution of this paper is an affirmative answer to the above question. We present a novel architecture, called BIRD, to handle CNF-XOR formulas arising from hashingbased techniques. The resulting hashing-based approximate model counter, called ApproxMC3, employs the BIRD framework in its underlying SAT solver, CryptoMiniSat. To the best of our knowledge, we conducted the most comprehensive study of evaluation performance of counting algorithms involving 1896 benchmarks with computational effort totaling 86400 computational hours. Our experimental evaluation demonstrates significant runtime performance improvement for ApproxMC3 over ApproxMC2. In particular, we solve 648 benchmarks more than ApproxMC2, the state of the art approximate model counter and for all the formulas where both ApproxMC2 and ApproxMC3 did not timeout and took more than 1 seconds, the mean speedup is 284.40 – more than two orders of magnitude.

scholarly journals Report from the 1st Int. Workshop on Education through Advanced Software Engineering and Artificial Intelligence (EASEAI '19)

2020 ◽  
Vol 45 (1) ◽  
pp. 25-27
Author(s):  
Benoît Vanderose ◽  
Benoît Frenay ◽  
Julie Henry ◽  
Xavier Devroey
Keyword(s):  
Artificial Intelligence ◽  
Software Engineering
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