5 papers
Integrating Machine Learning into Belief-Desire-Intention Agents: Current Advances and Open Challenges
Andrea Agiollo, Andrea Omicini
Thanks to the remarkable human-like capabilities of machine learning (ML) models in perceptual and cognitive tasks, frameworks integrating ML within rational agent architectures ar…
Concurrency Model of BDI Programming Frameworks: Why Should We Control It?
Martina Baiardi, Samuele Burattini, Giovanni Ciatto +3
We provide a taxonomy of concurrency models for BDI frameworks, elicited by analysing state-of-the-art technologies, and aimed at helping both BDI designers and developers in makin…
On the external concurrency of current BDI frameworks for MAS
Martina Baiardi, Samuele Burattini, Giovanni Ciatto +3
The execution of Belief-Desire-Intention (BDI) agents in a Multi-Agent System (MAS) can be practically implemented on top of low-level concurrency mechanisms that impact on efficie…
Symbolic Knowledge Extraction and Injection with Sub-symbolic Predictors: A Systematic Literature Review
Giovanni Ciatto, Federico Sabbatini, Andrea Agiollo +2
In this paper we focus on the opacity issue of sub-symbolic machine learning predictors by promoting two complementary activities, namely, symbolic knowledge extraction (SKE) and i…
Large language models as oracles for instantiating ontologies with domain-specific knowledge
Giovanni Ciatto, Andrea Agiollo, Matteo Magnini +1
Background. Endowing intelligent systems with semantic data commonly requires designing and instantiating ontologies with domain-specific knowledge. Especially in the early phases,…