Knowledge Representation in Empathic Robots-Rappresentazione della conoscenza in robot empatici

In questo articolo si illustra l'architettura cognitiva di un robot umanoide basato sul paradigma della Latent Semantic Analysis (LSA). L'approccio LSA consente la creazione e l'utilizzo di un spazio concettuale multi-dimensionale e data driven. Questo paradigma è un passo verso la simulazione di un comportamento emotivo di un robot che interagisce con gli umani. L'architettura è organizzata in tre aree principali: Subconcettuale, emotivo e comportamentale. La prima area elabora i dati percettivi provenienti dai sensori. La seconda area è lo "spazio concettuale di stati emotivi" che costituisce la rappresentazione sub-simbolica di emozioni. L'ultima area attiva un comportamento semantico la…

A Subsymbolic Approach to Word Modeling for Domain Specific Speech Recognition

Vision and emotional flow in a cognitive architecture for human-machine interaction

The detection and recognition of a human face should meet the need for social interaction that drives a humanoid robot, and it should be consistent with its cognitive model and the perceived scene. The paper deals with the description of the potential of having a system of emotional contagion, and proposes a simple implementation of it. An emotional index allows to build a mechanism which tends to align the emotional states of the robot and the human when a specific object is detected in the scene. Pursuing the idea of social interaction based on affect recognition, a first practical application capable of managing the emotional flow is described, involving both conceptual spaces and an emo…

An Intelligent Agent to Support City Policies Decisions

AN ARCHITECTURE FOR HUMANOID ROBOT EXPRESSING EMOTIONS AND PERSONALITY

In this paper we illustrate the cognitive architecture of a humanoid robot based on the proposed paradigm of Latent Semantic Analysis (LSA). The LSA approach allows the creation and the use of a data driven high-dimensional conceptual space. This paradigm is a step towards the simulation of an emotional behavior of a robot interacting with humans. The Architecture is organized in three main areas: Sub-conceptual, Emotional and Behavioral. The first area processes perceptual data coming from the sensors. The second area is the “conceptual space of emotional states” which constitutes the sub-symbolic representation of emotions. The last area activates a latent semantic behavior related to the…

Towards a Semantic Information Encoding and Retrieval in Relational Database

A Virtual Shopper Customer Assistant in Pervasive Environments.

A Semantic Information Retrieval in a Robot Museum Guide Application

Predicting Outcome of Type B Aortic Dissection with Patent False Lumen by Computational Flow Analysis