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GitHub - informalsystems/quint: An executable specification language with delightful tooling based on the temporal logic of actions (TLA)

An executable specification language with delightful tooling based on the temporal logic of actions (TLA) - GitHub - informalsystems/quint: An executable specification language with delightful tooling based on the temporal logic of actions (TLA) ....

Thomas Pani , Igor Konnov , Shon Feder , Ivan Gavran , Philip Offtermatt , Gabriela Moreira , Romain Ruetschi , Ranadeep Biswas , Vienna Business Agency , Temporal Logic , Secret Santa , Jure Kukovec , Vienna Business ,

Ask HN: What's your favorite GPT powered tool?

Ask HN: What's your favorite GPT powered tool?
ycombinator.com - get the latest breaking news, showbiz & celebrity photos, sport news & rumours, viral videos and top stories from ycombinator.com Daily Mail and Mail on Sunday newspapers.

United States , Tennessee Library Association , Purpose Technology , Raycast Pro , Code Step , Copilot Chat , Three Letter Acronym , Temporal Logic , General Purpose Technology ,

Linux Foundation launches TLA+ language foundation

TLA+ is a high-level programming language used to model complex, concurrent, and distributed programs and systems. It was created by Leslie Lamport and is backed by Amazon Web Services, Microsoft, and Oracle. ....

Leslie Lamport , Linux Foundation On , Amazon Web Services , Microsoft Research , Linux Foundation , Temporal Logic , Oracle Cloud Infrastructure ,

"Lifelong reinforcement learning with temporal logic formulas and rewar" by Xuejing Zheng, Chao Yu et al.

Continuously learning new tasks using high-level ideas or knowledge is a key capability of humans. In this paper, we propose lifelong reinforcement learning with sequential linear temporal logic formulas and reward machines (LSRM), which enables an agent to leverage previously learned knowledge to accelerate the learning of logically specified tasks. For a more flexible specification of tasks, we first introduce sequential linear temporal logic (SLTL), which is a supplement to the existing linear temporal logic (LTL) formal language. We then utilize reward machines (RMs) to exploit structural reward functions for tasks encoded with high-level events, and propose an automatic extension of RMs and efficient knowledge transfer over tasks for continuous lifelong learning. Experimental results show that LSRM outperforms methods that learn the target tasks from scratch by taking advantage of the task decomposition using SLTL and the knowledge transfer over RMs during the lifelong learning pr ....

Lifelong Reinforcement Learning , Reward Machine , Temporal Logic ,