Logic ecosystems meet meta-interpretivative learning: design and experiments on 2p-Kt
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@mastersthesis{2pktnannini2022,
amspractice = {2442694},
author = {Nannini, Luca},
available = {2022-04-11},
cosupervisor = {Ciatto, Giovanni},
cycle = {LM},
description = {This thesis is rooted in the field of Inductive Logic Programming (ILP), and, in particular, Meta-Interpretative Learning (MIL). ILP is a branch of Machine Learning where the Artificial Intelligence tries to induce Horn clauses from a given background knowledge and some positive/negative examples. The goal of this thesis is the development of a system for assisting interpretative learning algorithms. In order to achieve that, we exploit 2p-kt, an ecosystem for Symbolic Artificial Intelligence, providing an extension of the latter for supporting the concept of MetaRule and we design and implement a system of pluggable components aiming to assist the various steps of ILP algorithms (such as generalization of induced rules and refinement of theories). The results are: a 2p-kt based library of various generalization, validation and refinement strategies, a brand new algorithm inspired by Metagol (named MetaPatrol) and a test suite. The system poses as a 2p-kt extension supporting the definition of MetaRules, different mechanisms of generalization, the validation and refinement of induced theories as first class mechanisms, as a whole allowing the engineering of multiple strategies of MIL},
language = {en},
month = may,
start = {2022-04-15},
supervisor = {Omicini, Andrea},
title = {Logic ecosystems meet meta-interpretivative learning: design and experiments on 2p-Kt},
type = {Master's thesis},
url = {https://github.com/pikalab-unibo/master-thesis-nannini-ay2122},
year = 2022
}
amspractice = {2442694},
author = {Nannini, Luca},
available = {2022-04-11},
cosupervisor = {Ciatto, Giovanni},
cycle = {LM},
description = {This thesis is rooted in the field of Inductive Logic Programming (ILP), and, in particular, Meta-Interpretative Learning (MIL). ILP is a branch of Machine Learning where the Artificial Intelligence tries to induce Horn clauses from a given background knowledge and some positive/negative examples. The goal of this thesis is the development of a system for assisting interpretative learning algorithms. In order to achieve that, we exploit 2p-kt, an ecosystem for Symbolic Artificial Intelligence, providing an extension of the latter for supporting the concept of MetaRule and we design and implement a system of pluggable components aiming to assist the various steps of ILP algorithms (such as generalization of induced rules and refinement of theories). The results are: a 2p-kt based library of various generalization, validation and refinement strategies, a brand new algorithm inspired by Metagol (named MetaPatrol) and a test suite. The system poses as a 2p-kt extension supporting the definition of MetaRules, different mechanisms of generalization, the validation and refinement of induced theories as first class mechanisms, as a whole allowing the engineering of multiple strategies of MIL},
language = {en},
month = may,
start = {2022-04-15},
supervisor = {Omicini, Andrea},
title = {Logic ecosystems meet meta-interpretivative learning: design and experiments on 2p-Kt},
type = {Master's thesis},
url = {https://github.com/pikalab-unibo/master-thesis-nannini-ay2122},
year = 2022
}
