Enhancing Symbolic AI Ecosystems with Probabilistic Logic Programming: a Kotlin Multi-Platform Case Study

Jason Dellaluce

As Arti cial Intelligence (AI) progressively conquers the software industry at a fast
pace, the demand for more transparent and pervasive technologies increases accordingly.
In this scenario, novel approaches to Logic Programming (LP) and symbolic
AI have the potential to satisfy the requirements of modern software environments.
However, traditional logic-based approaches often fail to match present-day planning
and learning work
ows, which natively deal with uncertainty. Accordingly,
Probabilistic Logic Programming (PLP) is emerging as a modern research eld
that investigates the combination of LP with the probability theory. Although
research e orts at the state of the art demonstrate encouraging results, they are
usually either developed as proof of concepts or bound to speci c platforms, often
having inconvenient constraints. In this dissertation, we introduce an elastic and
platform-agnostic approach to PLP aimed to surpass the usability and portability
limitations of current proposals. We design our solution as an extension of the 2PKt
symbolic AI ecosystem, thus endorsing the mission of the project and inheriting
its multi-platform and multi-paradigm nature. Additionally, our proposal comprehends
an object-oriented and pure-Kotlin library for manipulating Binary Decision
Diagrams (BDDs), which are notoriously relevant in the context of probabilistic
computation. As a Kotlin multi-platform architecture, our BDD module aims
to surpass the usability constraints of existing packages, which typically rely on
low level C/C++ bindings for performance reasons. Overall, our project explores
novel directions towards more usable, portable, and accessible PLP technologies,
which we expect to grow in popularity both in the research community and in the
software industry over the next few years.



Enhancing Symbolic AI Ecosystems with Probabilistic Logic Programming: a Kotlin Multi-Platform Case Study

— author

Jason Dellaluce


— supervisor

Roberta Calegari

— co-supervisor

Giovanni Ciatto


— cycle

second-cycle thesis

— status

completed thesis

— language



— degree date


IDs & URLs




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