A Modular System Oriented to the Design of Versatile Knowledge Bases for Chatbots

The paper illustrates a system that implements a framework, which is oriented to the development of a modular knowledge base for a conversational agent. This solution improves the flexibility of intelligent conversational agents in managing conversations. The modularity of the system grants a concurrent and synergic use of different knowledge representation techniques. According to this choice, it is possible to use the most adequate methodology for managing a conversation for a specific domain, taking into account particular features of the dialogue or the user behavior. We illustrate the implementation of a proof-of-concept prototype: a set of modules exploiting different knowledge representation methodologies and capable of managing different conversation features has been developed. Each module is automatically triggered through a component, named corpus callosum, that selects in real time the most adequate chatbot knowledge module to activate

A Modular System Oriented to the Design of Versatile Knowledge Bases for Chatbots

The paper illustrates a system that implements a framework, which is oriented to the development of a modular knowledge base for a conversational agent. This solution improves the flexibility of intelligent conversational agents in managing conversations. The modularity of the system grants a concurrent and synergic use of different knowledge representation techniques. According to this choice, it is possible to use the most adequate methodology for managing a conversation for a specific domain, taking into account particular features of the dialogue or the user behavior. We illustrate the implementation of a proof-of-concept prototype: a set of modules exploiting different knowledge representation methodologies and capable of managing different conversation features has been developed. Each module is automatically triggered through a component, named corpus callosum, that selects in real time the most adequate chatbot knowledge module to activate

Sensing the Web for Induction of Association Rules and their Composition through Ensemble Techniques

Abstract Starting from geophysical data collected from heterogeneous sources, such as meteorological stations and information gathered from the web, we seek unknown connections between the sampled values through the extraction of association rules. These rules imply the co-occurrence of two or more symbols in the same representation, and the rule confidence may vary according to the collected data. We propose, starting from traditional algorithms such as FP-Growth and Apriori, the creation of complex association rules through boosting of simpler ones. The composition enables the creation of rules that are robust and let emerge a larger number of interesting rules

A Knowledge Management and Decision Support Model for Enterprises

We propose a novel knowledge management system (KMS) for enterprises. Our system exploits two different approaches for knowledge representation and reasoning: a document-based approach based on data-driven creation of a semantic space and an ontology-based model. Furthermore, we provide an expert system capable of supporting the enterprise decisional processes and a semantic engine which performs intelligent search on the enterprise knowledge bases. The decision support process exploits the Bayesian networks model to improve business planning process when performed under uncertainty

Social signs processing in a cognitive architecture for an humanoid robot

Abstract A social robot has to recognize human social intention in order to fully interact with him/her. People intention can be inferred by processing verbal and non-verbal communicative signs. In this work we describe an actions classification module embedded into a robot's cognitive architecture, contributing to the interpretation of users behavior

AIxPAC 2023 - Preface to the 1st Workshop on Artificial Intelligence for Perception and Artificial Consciousness

The AIxPAC workshop aims to bring together researchers from academia and industry to discuss the latest advancements in AI for perception and consciousness. The workshop features presentations from experts on the physicalist ontology of consciousness, artificial consciousness, colour perception, and computer vision. Some research questions are addressed in AIxPAC: Can a visual perception system be embedded into machines? How accurately does AI tackle visual attention processes? What is the relation between attention and consciousness? Can AI architectures and approaches be used to design Artificial Consciousness? What are the pros and cons of Large Language Models? The given research questions foster multidisciplinary contributions and several critical readings for the given topics. © 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0)