CAREER: A Software Development Framework That Integrates Learning, Probabilistic Reasoning, And Any-Time Computation

如今越来越多的计算机配备了传感器和执行器，这样它们就可以直接与人和外部世界进行交互。具体的例子包括通过互联网与人和其他软件代理交互的软件代理，以及与人和物理世界交互的消费产品。这种计算机通常被称为“嵌入式”，面临着各种挑战。例如，传感器的局限性可能使其无法准确地感知环境的状态;因此，嵌入式计算机必须能够科普不确定性。嵌入式计算机的环境可能是动态的，在这种情况下，需要适应其中的变化。此外，嵌入式计算机可能必须满足实时约束，因为它们的环境往往不会等待程序的终止。虽然这些问题中的许多已经在人工智能（AI）领域进行了研究，但计算机科学仍然缺乏用于嵌入式计算机软件开发的合理方法。现有的编程语言和工具包不能解决诸如不确定性、适应性和实时计算等问题。PI的职业目标是改变嵌入式计算机的编程方式。为了实现这一目标，他建议研究一种新的编程语言，专门为嵌入式计算机系统设计。这种语言将引入三个新的想法，目前没有发现在现有的编程语言：I。 概率数据类型和运算符。概率数据类型和运算符将使程序员能够像计算确定信息一样计算不确定信息。概率数据类型通过概率密度表示信息。它们概括了现有的数据类型（例如，float，int），因为它们一次表示多个值，并按数值概率加权。从程序员的角度来看，概率计算将类似于传统的计算，增加了对不确定性的鲁棒性。 适应。PI将开发支持程序代码的数据驱动适应的机制。语言将拥有内置的学习算法（例如，神经网络、强化学习）和用于适当的信用分配的机制。这将有助于设计适应性强的软件，能够自动适应环境的变化。它还将使程序员能够“教”他们的代码，作为对当前软件开发实践的补充。 随时执行。为了满足及时响应的需要，新语言将为任何时间计算提供运行时支持。随时程序可以在（几乎）任何时间查询结果;然而，其解决方案的质量会随着时间的推移而增加。为了获得任意时间的特性，执行系统将有选择地处理概率变量，以个体值的概率降序。如果这项研究成功，嵌入式系统的程序员将能够以最小的开销利用这项研究成果，从而有效地使他们能够开发出比当今最佳实践更好的软件。为了更好地实现他的职业目标，PI还将参与一系列教育活动。他计划修改CMU的入门编程课程，包括嵌入式计算的课程部分。他还将开发一个新的嵌入式计算研究生课程，该课程将整合目前各种独立学科的材料。对于这两门课程，编程语言将被用作一种工具：通过使用这种语言，学生将更深入地了解嵌入式计算及其理论基础所涉及的各种问题。此外，高级学生的反馈将指导这里提出的基础研究。所有的课程材料，语言，它的实施和文件，以及所有相关的研究论文将提供给研究界在广大使用网络，使其他人可以促进和受益于这个项目。

An increasing number of today's computers are equipped with sensors and actuators, so that they can interact directly with people and the outside world. Specific examples include software agents that interact with people and other software agents through the Internet, and consumer products, which interact with people and the physical world. Such computers, often called "embedded," face a variety of challenges. For example, sensor limitations might make it impossible to sense the state of the environment accurately; thus, embedded computers must be able to cope with uncertainty. The environments of embedded computers might be dynamic, in which case there is a need to adapt to changes therein. Additionally, embedded computers might have to meet real-time constraints, as their environments tend not to wait for the termination of their programs. While many of these issues have been researched in the field of artificial intelligence (AI), computer science still lacks a sound methodology for software development in embedded computers. Existing programming languages and toolkits do not address issues such as uncertainty, adaptation, and on-time computing.The PI's career objective it to change the way embedded computers are programmed. To pursue this goal, he proposes research towards a new programming language, specifically designed for embedded computer systems. This language will introduce three new ideas currently not found in existing programming languages:I. Probabilistic data types and operators. Probabilistic data types and operators will enable programmers to compute with uncertain information just as if it was certain. Probabilistic data types represent information by probability densities. They generalize existing data types (e.g., float, int) in that they represent multiple values at a time, weighted by numerical probabilities. From the programmer's view, probabilistic computation will be analogous to conventional computation, with the added benefit of increased robustness to uncertainty.2. Adaptation. The PI will develop mechanisms that support data-driven adaptation of program code. language will possess built-in learning algorithms (e.g., neural networks, reinforcement learning) and a mechanism for proper credit assignment. This will support the design of adaptable software, capable of automatically adapting to changes in the environment. It will also enable programmers to "teach" their code, as a supplement to current software development practice.3. Any-time execution. To accommodate the need for timely responses, the new language will provide run-time support for any-time computation. Any-time programs can be queried for a result at (almost) any time; the quality of their solutions, however, increases over time. To obtain any-time characteristics, the execution system will process probabilistic variables selectively, in decreasing order of the probability of individual values. A special mechanism will be provided that permits the event-driven termination of any-time computation.If this research is successful, programmers of embedded systems will be able to utilize the results-with minimal overhead, effectively enabling them to develop better software with significantly less effort, when compared to today's best practice.To better achieve his career goal, the PI will also engage in a collection of educational activities. He plans to revise the curriculum of CMU's introductory programming class, by including a course segment on embedded computation. He will also develop a new graduate-level course on embedded computation, which will integrate material from a variety of currently separate disciplines. For both courses, the programming language will be used as a vehicle: By using this language, students will gain a much deeper understanding of the various issues involved in embedded computation and their theoretical foundations. Additionally, feedback of advanced students will guide the basic research proposed here. All course materials, the language, its implementation and documentation, and all related research papers will be made available to the research community at large using the Web, so that others can contribute to and benefit from this project.