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.
