A Language and Logic for Programming and Reasoning with Partial Observability

Computer systems are increasingly deployed in partially-observable environments, in which the system cannot directly determine the environment’s state but receives partial information from observations. When such a computer system executes, it risks forming an incorrect belief about the true state of the environment. For example, the Mars Polar Lander (MPL) is a lost space probe that crashed because its control software believed it was on the Martian surface when it was actually 40m high, and as a result, it turned off its descent engine too early. Developers need better software development tools to prevent such accidents. In this dissertation, I will present programming language tools that enable developers to deliver correct software in partially-observable environments. In particular, I will present belief programming, a new type of programming language in which developers write a model of the partial observability in the environment. The language produces an executable state estimator, which is a function that maps environmental observations to estimates of the environment’s true state. I have implemented the prototype belief programming language BLIMP, and used BLIMP to implement several example programs – including an engine controller for the MPL. I will also present Epistemic Hoare Logic (EHL), a program logic for belief programs that enables developers to reason about properties of the resulting state estimators. I have used EHL to prove that the BLIMP version of the MPL does not have the error that caused the original MPL to crash. Furthermore, I will present semi-symbolic inference, a technique that provides more efficient implementations of belief programming languages. Across a range of benchmarks, my performance experiments show that a semi-symbolic BLIMP implementation achieves speedups of 515x-58,919x over a naïve BLIMP implementation. Together, the contributions of belief programming, EHL, and semi-symbolic inference enable developers to focus on modeling the partial observability in the environment, and provide programming language support for automatically generating and reasoning about efficient state estimators.

Name thesis:degree_name

Doctoral

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2024

Author dc:creator

• Atkinson, Eric Hamilton

Advisor dc:contributor.advisor

• Carbin, Michael

Statement dc:rights

• In Copyright - Educational Use Permitted
• Copyright retained by author(s)

Licence dc:rights.uri

• https://rightsstatements.org/page/InC-EDU/1.0/

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/153840

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/153840