Gianmarco Tuccini

This study explores the intricate realm of human pain, aiming to construct an ontological model that seeks to describe, at least in part, a possible structure of this profound and complex phenomenon. Drawing from philosophical insights and diverse scholarly perspectives, it seeks to broaden our understanding of pain’s nuanced dimensions as depicted in textual datasets. The research employs a novel annotation method for pain-related texts, leveraging the depth of understanding offered by affect theory to enrich the semantic value of the annotations. Our methodology adopts an ontological approach to analyzing written accounts of personal and phenomenological experiences of pain, both from individuals currently experiencing pain and those recalling past painful experiences. These annotations form a structured and nuanced dataset that mirrors the complexities of subjective and qualitative human experiences. The study delves into various fundamental aspects of pain experience, such as its temporal, emotional, bodily, evaluative, and semantic-stylistic structure. This enriches the annotation process, providing a robust framework to interpret and account for the multidimensional nature of the pain phenomenon. The findings unveil a layered and interconnected portrayal of pain experiences, illuminating the complex interplay between affective states, contextual factors, and individual perceptions. The discussion section contemplates the implications of the ontological model, underscoring its potential applications in various fields, including healthcare and artificial intelligence. The research underscores the significance of integrating philosophical theories into empirical endeavors, demonstrating the value of a multidisciplinary approach in deciphering the complexities of subjective human experiences. This study, therefore, serves as a bridge between the abstract world of philosophy and the tangible realm of empirical research, shedding light on the qualitative experience of pain through the lens of phenomenology.

The automation of many repetitive or dangerous human activities yields numerous advantages. In order to automate a physical task that requires a finite series of sequential steps, the translation of those steps in terms of a computational procedure is often required. Even apparently menial tasks like following a cooking recipe may involve complex operations that can’t be perfectly described in formal terms. Recently, several studies have explored the possibility to model cooking recipes as a computational procedure based on a set of instructions. This vision is the foundation for the construction of robotics kitchen, as Moley. These kitchen robots have shown promising results. Moley, for instance, is the very first example of a new generation of bioinspired robotics, based on the reproduction of particular movement, cooking, through artificial arms and hands. It is entirely different from the current appliances present in our domestic domain because Moley is able to manipulate ingredients and interact with kitchen equipment in order to prepare a dish autonomously. Nonetheless, they have also shown several limitations. In particular, Moley is an entirely autonomous robot in a structured environment in which it knows precisely the object position and manipulates the ingredients based on a list of instructions and a training phase made by Machine Learning. In this contribution we contend that these limitations arise from an essential mismatch between computational procedures, as originally described by Turing in his seminal 1936 paper, and recipes. Computational procedures have been originally created to observe and modify formal symbols with formal operators. Thus, they are independent from time and they are ideally executed in a closed environment, in which the computer directly produces all the relevant changes required to produce the intended result. Recipes, instead, are followed in an open environment, in which, while time goes by, changes happen independently from the cook’s intervention: the cook puts the butter on the pan, starts the fire and then waits until the butter is melted. To operate effectively in the open environment a kitchen robot must couple computational procedures with sensors, e.g a sensor which provides a time signal. These sensors are de facto oracles for the procedures and yet are required to bridge the gap from the formal to the physical world.

Computational models of affect (CMAS), in their most common form, cannot take into account the qualitative (phenomenal) dimension of affect itself. Their expressivity can be extended, thus promoting the much sought-after standardization in the most theory-neutral way, using OWL (Web Ontology Language) and machine learning techniques. OWL is an expressive formal language, as well as an established open standard, and can be used to describe the models, possibly including qualitative entities at the fundamental level. The supervised machine learning techniques allow the direct learning and application of models described as OWL ontologies. Thanks to human supervision (e.g. using datasets labeled by a human user), they can take into account the qualitative dimension of affect when the models warrant it. To further promote the aforementioned standardization, the task of classifying texts according to their affective content (known in computer science as "sentiment analysis") can be recommended as a way to assess the performance of the models. It is a multifaceted task, in which usually divergent fields as philosophy, psychology and computer science meet. Moreover, since it is a very current task in computer science, there are many resources available to facilitate the development of a standard benchmark for CMAS.