by Giovanni Ciatto and Lorenzo Rizzato
Computational logic and logic programming (LP) play a central role in AI. While their impact on symbolic AI is well established, emergent AI techniques leverage on logic to make data-driven AI more predictable or understandable. Thus, the need for solid, interoperable, general-purpose logic-based technologies (LBT) is more compelling than ever.
Most LBT are either built on top or as extensions of the Prolog language. Even when this is not the case, monolithic solutions are built around different inference procedures, unification mechanisms, or knowledge representation techniques. However, LBT should be neither constructed as Prolog-centred monoliths nor tailored to a specific semantics or language. To maximise their AI-related impact, LBT should welcome the manifold contributions coming from the LP playground, supporting the general-purposes exploitation of as many mechanisms as possible.
Accordingly, we present 2P-Kt, a reboot of the tuProlog project offering a general, extensible, and interoperable ecosystem for LP and symbolic AI.
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tuProlog homepage: http://tuprolog.unibo.it
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2P-Kt project on GitHub: https://github.com/tuProlog/2p-kt
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Recording of the talk (requires Unibo credentials, ask for a copy via a private email otherwise):
- Part 1: https://web.microsoftstream.com/video/f53478b2-d628-4ce5-ac8e-f59c4d5ed14a (roughly, slides 1-182)
- Part 2: https://web.microsoftstream.com/video/500573d3-b472-4ce4-acb0-bbcfa2c3d625 (roughly, slides 182-334)
- Part 3: https://web.microsoftstream.com/video/a8b0b81b-95c8-4377-9c5d-c46776c83fea (roughly, slides 334-425)