Temporal logic has become an essential tool of many AI applications, from verification to planning to synthesis. Mission-time Linear Temporal Logic (MLTL) adds closed-interval integer bounds on the temporal operators of LTL, enabling unit-agnostic specification over finite traces. It is arguably the most-used variation of MTL, and the most-used subset of STL in industrial and AI applications. MLTL optimizes the trade-off between expressibility of a wide range of realistic requirements and the ability to author generic, easy-to-validate formulas. We highlight successful AI applications centered around MLTL requirements, including Robonaut2 and the NASA Lunar Gateway Vehicle System Manager. We overview advances in analyzing MLTL, explain the motivation driving these developments, and point out the gaps in the state of the art where there are needs for future work.

 

FOAM seminars
The FOAM Seminar, organised by computer scientists at the ILLC, features research on questions of a fundamental nature in computer science and AI, in research areas such as algorithms, optimisation, data management, planning, knowledge representation, and multiagent systems. Talks are intended to be broadly accessible and pitched at the level you might find at a plenary talk of a relevant conference (such as IJCAI, AAAI, KR, ICAPS, AAMAS, EC, PODS, LICS, STOC, FOCS, and SODA).