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Indeed, generating Prolog for robotic and other discrete planning has been pursued. From [1]: > Rather than having LLMs perform planning itself, it's thus only

Indeed, generating Prolog for robotic and other discrete planning has been pursued. From [1]: > Rather than having LLMs perform planning itself, it's thus only natural to complement LLMs with the raw combinatorial power of Prolog, much like a human would be reaching out to a pocket calculator. [1]: https://quantumprolog.sgml.net/llm-demo/part1.html https://quantumprolog.sgml.net/llm-demo/part1.html

disambiguation 1y ago
seems interesting, though i'm not sure I fully understand - its mainly a demo where LLMs generate prolog code for this specific port problem? I was imagining my idea on a larger scale, 1. using prolog to improve general "reasoning" benchmarks (for ex. these kinds of problems https://www.llm-reasoning-benchmark.com/ https://www.llm-reasoning-benchmark.com/) and 2. using prolog in the context of an agent, as a way to model its environment and make better plans towards goals.
- tannhaeuser 1y ago
  It's a guide for prompting LLMs to generate Prolog for any problem that can be solved by a STRIPS-like formulation with state and robotic actions like the container packing and container shipping problems (and a few artificial ones from AI competitions). A main predicate encoding a goal selection strategy is omitted in generation and a generic one is supplied instead. The point is using language models to translate text descriptions into Prolog knowledge and state change representations guided by in-context examples, which was found to work really well in practice, and is also quite natural given Prolog's NLP roots.
  - disambiguation 1y ago
    Very interesting, TIL about STRIPS - but if this works as well as claimed then without knowing more it seems too good to be true, and further surprising that it isn't more widely adopted. I'm curious if you have plans for this site and code, or was it your own PoC? Like I mentioned I'm curious how it would hold up to more standard benchmarks. When I finally start my own prolog journey I'll remember to revisit your site so I can fully appreciate it, nonetheless thanks for sharing and explaining.

disambiguation 1y ago

seems interesting, though i'm not sure I fully understand - its mainly a demo where LLMs generate prolog code for this specific port problem? I was imagining my idea on a larger scale, 1. using prolog to improve general "reasoning" benchmarks (for ex. these kinds of problems https://www.llm-reasoning-benchmark.com/ https://www.llm-reasoning-benchmark.com/) and 2. using prolog in the context of an agent, as a way to model its environment and make better plans towards goals.

tannhaeuser 1y ago
It's a guide for prompting LLMs to generate Prolog for any problem that can be solved by a STRIPS-like formulation with state and robotic actions like the container packing and container shipping problems (and a few artificial ones from AI competitions). A main predicate encoding a goal selection strategy is omitted in generation and a generic one is supplied instead. The point is using language models to translate text descriptions into Prolog knowledge and state change representations guided by in-context examples, which was found to work really well in practice, and is also quite natural given Prolog's NLP roots.
- disambiguation 1y ago
  Very interesting, TIL about STRIPS - but if this works as well as claimed then without knowing more it seems too good to be true, and further surprising that it isn't more widely adopted. I'm curious if you have plans for this site and code, or was it your own PoC? Like I mentioned I'm curious how it would hold up to more standard benchmarks. When I finally start my own prolog journey I'll remember to revisit your site so I can fully appreciate it, nonetheless thanks for sharing and explaining.