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Adaptation-Based Programming

基于适应的编程

基本信息

批准号：
0820286
负责人：
Alan Fern
金额：
$ 74.59万
依托单位：
Oregon State University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2008
资助国家：
美国
起止时间：
2008-09-15 至 2012-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=0820286&HistoricalAwards=false
关键词：
Adaptation Based Programming

项目摘要

Proposal Number: 0820286Title: Adaptation-Based ProgrammingPI: Alan FernCo-PIs: Martin Erwig and Thinh NguyenDeveloping software for complex, dynamic environments is a huge challenge using existing programming paradigms. Programmers must specify the exact behavior of a program at each point which, for complex problems, such as network control and designing intelligent game agents, is extremely difficult to do close to optimally. To better facilitate software development for such problems, the paradigm of adaptation-based programming (ABP) is investigated. In the ABP paradigm, programmers only exactly specify the parts of the program that they are confident about and leave other parts as adaptable. In addition, the programmer will specify an objective function to be optimized by the program. During program execution, the adaptable parts of the program will then be automatically optimized using state-of-the-art machine-learning techniques in order to maximize the objective function. The research focuses on three key directions: 1) developing theoretical foundations including formal programming-language semantics and learnability results, 2) speeding up the learning process via program transformations and programmer-specified adaptation advice, and 3) producing freely available ABP libraries for both the C++ and Haskell languages. Importantly all of the work is driven by a focus on applications of ABP to real problems from computer networking and intelligent agents for simulation and game environments.

提案编号：0820286标题：基于适应的编程PI：Alan FernCo-PI：Martin Erwig和Thinh Nguyen使用现有的编程范例为复杂、动态的环境开发软件是一个巨大的挑战。程序员必须指定程序在每个点上的确切行为，对于复杂的问题，如网络控制和设计智能游戏代理，几乎很难做到最优。为了更好地促进此类问题的软件开发，研究了基于适应的编程(ABP)范式。在ABP范例中，程序员只精确地指定他们对程序的哪些部分有信心，而将其他部分保留为可适应的部分。此外，程序员还将指定程序要优化的目标函数。在程序执行期间，程序的可适应部分将使用最先进的机器学习技术自动优化，以便最大化目标函数。研究集中在三个主要方向：1)发展理论基础，包括形式化的编程语言语义和可学习性结果；2)通过程序转换和程序员指定的适配建议来加快学习过程；3)生成C++和Haskell语言的免费可用的ABP库。重要的是，所有的工作都是由对ABP应用于实际问题的关注推动的，这些问题来自计算机网络以及用于模拟和游戏环境的智能代理。