Prospecting a systemic design space for pandemic responses

Design literature describes an expansion of design activity towards systemic relations, which requires dealing with controversies among multiple actors. These controversies have a sociotechnical nature, given the inextricably of social and technical relations. This research looks at the sociotechnical controversy in COVID-19 design responses to assess the extent of said expansion. A controversial design space mapping was conducted based on a set of web pages found in the international design community which describes design responses for the pandemic. Considered as a representation of a socially-produced design space, the mapping reveals that systemic relations are still overlooked by the design community. A possible explanation for that is the lack of depth in problematizing the COVID-19 pandemic. The research offers, thus, prospective recommendations for a systemic design space for pandemics and other systemic crisis

A Model of Emotion as Patterned Metacontrol

Adaptive agents use feedback as a key strategy to cope with un- certainty and change in their environments. The information fed back from the sensorimotor loop into the control subsystem can be used to change four different elements of the controller: parameters associated to the control model, the control model itself, the functional organization of the agent and the functional realization of the agent. There are many change alternatives and hence the complexity of the agent’s space of potential configurations is daunting. The only viable alternative for space- and time-constrained agents —in practical, economical, evolutionary terms— is to achieve a reduction of the dimensionality of this configuration space. Emotions play a critical role in this reduction. The reduction is achieved by func- tionalization, interface minimization and by patterning, i.e. by selection among a predefined set of organizational configurations. This analysis lets us state how autonomy emerges from the integration of cognitive, emotional and autonomic systems in strict functional terms: autonomy is achieved by the closure of functional dependency. Emotion-based morphofunctional systems are able to exhibit complex adaptation patterns at a reduced cognitive cost. In this article we show a general model of how emotion supports functional adaptation and how the emotional biological systems operate following this theoretical model. We will also show how this model is also of applicability to the construction of a wide spectrum of artificial systems1

Méthodes d'apprentissage inspirées de l'humain pour un tuteur cognitif artificiel

Les systèmes tuteurs intelligents sont considérés comme un remarquable concentré de technologies qui permettent un processus d'apprentissage. Ces systèmes sont capables de jouer le rôle d'assistants voire même de tuteur humain. Afin d'y arriver, ces systèmes ont besoin de maintenir et d'utiliser une représentation interne de l'environnement. Ainsi, ils peuvent tenir compte des évènements passés et présents ainsi que de certains aspects socioculturels. Parallèlement à l'évolution dynamique de l'environnement, un agent STI doit évoluer en modifiant ses structures et en ajoutant de nouveaux phénomènes. Cette importante capacité d'adaptation est observée dans le cas de tuteurs humains. Les humains sont capables de gérer toutes ces complexités à l'aide de l'attention et du mécanisme de conscience (Baars B. J., 1983, 1988), et (Sloman, A and Chrisley, R., 2003). Toutefois, reconstruire et implémenter des capacités humaines dans un agent artificiel est loin des possibilités actuelles de la connaissance de même que des machines les plus sophistiquées. Pour réaliser un comportement humanoïde dans une machine, ou simplement pour mieux comprendre l'adaptabilité et la souplesse humaine, nous avons à développer un mécanisme d'apprentissage proche de celui de l'homme. Ce présent travail décrit quelques concepts d'apprentissage fondamentaux implémentés dans un agent cognitif autonome, nommé CTS (Conscious Tutoring System) développé dans le GDAC (Dubois, D., 2007). Nous proposons un modèle qui étend un apprentissage conscient et inconscient afin d'accroître l'autonomie de l'agent dans un environnement changeant ainsi que d'améliorer sa finesse. ______________________________________________________________________________ MOTS-CLÉS DE L’AUTEUR : Apprentissage, Conscience, Agent cognitif, Codelet

On defining affects computationally

One of the most tangled fields of research is the field of defining and modeling affective concepts, i. e. concepts regarding emotions and feelings. The subject can be approached from many disciplines. The main problem is lack of generally approved definitions. However, e.g. linguists have recently started to check the consistency of their theories with the help of computer simulations. Definitions of affective concepts are needed for performing similar simulations in behavioral sciences. In this thesis, preliminary computational definitions of affects for a simple utility-maximizing agent are given. The definitions have been produced by synthetizing ideas from theories from several fields of research. The class of affects is defined as a superclass of emotions and feelings. Affect is defined as a process, in which a change in an agent's expected utility causes a bodily change. If the process is currently under the attention of the agent (i.e. the agent is conscious of it), the process is a feeling. If it is not, but can in principle be taken into attention (i.e. it is preconscious), the process is an emotion. Thus, affects do not presuppose consciousness, but emotions and affects do. Affects directed at unexpected materialized (i.e. past) events are delight and fright. Delight is the consequence of an unexpected positive event and fright is the consequence of an unexpected negative event. Affects directed at expected materialized (i.e. past) events are happiness (expected positive event materialized), disappointment (expected positive event did not materialize), sadness (expected negative event materialized) and relief (expected negative event did not materialize). Affects directed at expected unrealized (i.e. future) events are fear and hope. Some other affects can be defined as directed towards originators of the events. The affect classification has also been implemented as a computer program, the purpose of which is to ensure the coherence of the definitions and also to illustrate the capabilities of the model. The exact content of bodily changes associated with specific affects is not considered relevant from the point of view of the logical structure of affective phenomena. The utility function need also not be defined, since the target of examination is only its dynamics.Affektiivisten eli emootioihin ja tunteisiin liittyvien käsitteiden määrittely ja mallintaminen on tällä hetkellä eräs sekavimmista tutkimusaloista. Aihetta voidaan lähestyä monen eri tieteenalan kautta. Suurin ongelma on yhteisesti hyväksyttyjen määritelmien puute. Teorioiden toimivuutta ja sisäistä johdonmukaisuutta on muun muassa kielitieteessä ryhdytty tarkastamaan laskennallisten simulaatioiden avulla. Affektiivisten käsitteiden määritelmiä tarvittaisiin vastaavien simulaatioiden tekemiseksi käyttäytymistieteissä. Tässä tutkielmassa esitetään yksinkertaiselle hyötyä maksimoivalle toimijalle eli agentille soveltuvat affektien alustavat laskennalliset määritelmät. Määritelmät on tuotettu syntetisoimalla ja valikoimalla ajatuksia useista eri teorioista. Affektien luokka määritellään emootioiden ja tunteiden luokkien yläluokaksi. Affekti määritellään prosessiksi, jossa toimijan hyödyn muutos aiheuttaa vakioisen kehollisen muutoksen. Jos muutosprosessi on tarkasteluhetkellä toimijan tarkkaavaisuuden kohteena eli toimija on tietoinen siitä, kyseessä on tunne. Jos muutosprosessi on periaatteessa otettavissa tarkkaavaisuuden kohteeksi eli se on esitietoinen, kyseessä on emootio. Affektit eivät siis edellytä tietoisuutta, mutta emootiot ja tunteet edellyttävät. Esimerkiksi odottamattomiin toteutuneisiin eli menneisiin tapahtumiin kohdistuvia affekteja ovat ilahtuminen ja säikähdys. Ilahtuminen on odottamattoman positiivisen tapahtuman seuraus ja säikähdys vastaavasti odottamattoman negatiivisen tapahtuman seuraus. Odotettuihin toteutuneisiin tapahtumiin kohdistuvia affekteja ovat onni (odotettu positiivinen tapahtuma toteutui), pettymys (odotettu positiivinen tapahtuma ei toteutunut), suru (odotettu negatiivinen tapahtuma toteutui) ja helpotus (odotettu negatiivinen tapahtuma ei toteutunut). Odotettuihin toteumattomiin (tuleviin) tapahtumiin kohdistuvia affekteja ovat vastaavasti pelko ja toivo. Eräitä muita affekteja voidaan määritellä edellä mainittujen tapahtumien aiheuttajiin kohdistuviksi. Affektiluokituksesta on tehty myös ohjelmatoteutus, jonka tarkoituksena on sekä varmistaa määritelmien koherenttius että havainnollistaa mallin mahdollisuuksia. Affekteihin liittyvien kehollisten muutosten täsmällistä sisältöä ei pidetä ilmiön loogisen rakenteen kannalta merkityksellisenä. Hyötyfunktiotakaan ei tarvitse määritellä, koska tarkastelun kohteena on ainoastaan sen dynamiikka

CernoCAMAL : a probabilistic computational cognitive architecture

This thesis presents one possible way to develop a computational cognitive architecture, dubbed CernoCAMAL, that can be used to govern artificial minds probabilistically. The primary aim of the CernoCAMAL research project is to investigate how its predecessor architecture CAMAL can be extended to reason probabilistically about domain model objects through perception, and how the probability formalism can be integrated into its BDI (Belief-Desire-Intention) model to coalesce a number of mechanisms and processes. The motivation and impetus for extending CAMAL and developing CernoCAMAL is the considerable evidence that probabilistic thinking and reasoning is linked to cognitive development and plays a role in cognitive functions, such as decision making and learning. This leads us to believe that a probabilistic reasoning capability is an essential part of human intelligence. Thus, it should be a vital part of any system that attempts to emulate human intelligence computationally. The extensions and augmentations to CAMAL, which are the main contributions of the CernoCAMAL research project, are as follows: - The integration of the EBS (Extended Belief Structure) that associates a probability value with every belief statement, in order to represent the degrees of belief numerically. - The inclusion of the CPR (CernoCAMAL Probabilistic Reasoner) that reasons probabilistically over the goal- and task-oriented perceptual feedback generated by reactive sub-systems. - The compatibility of the probabilistic BDI model with the affect and motivational models and affective and motivational valences used throughout CernoCAMAL. A succession of experiments in simulation and robotic testbeds is carried out to demonstrate improvements and increased efficacy in CernoCAMAL’s overall cognitive performance. A discussion and critical appraisal of the experimental results, together with a summary, a number of potential future research directions, and some closing remarks conclude the thesis

CernoCAMAL : a probabilistic computational cognitive architecture

This thesis presents one possible way to develop a computational cognitive architecture, dubbed CernoCAMAL, that can be used to govern artificial minds probabilistically. The primary aim of the CernoCAMAL research project is to investigate how its predecessor architecture CAMAL can be extended to reason probabilistically about domain model objects through perception, and how the probability formalism can be integrated into its BDI (Belief-Desire-Intention) model to coalesce a number of mechanisms and processes.The motivation and impetus for extending CAMAL and developing CernoCAMAL is the considerable evidence that probabilistic thinking and reasoning is linked to cognitive development and plays a role in cognitive functions, such as decision making and learning. This leads us to believe that a probabilistic reasoning capability is an essential part of human intelligence. Thus, it should be a vital part of any system that attempts to emulate human intelligence computationally.The extensions and augmentations to CAMAL, which are the main contributions of the CernoCAMAL research project, are as follows:- The integration of the EBS (Extended Belief Structure) that associates a probability value with every belief statement, in order to represent the degrees of belief numerically.- The inclusion of the CPR (CernoCAMAL Probabilistic Reasoner) that reasons probabilistically over the goal- and task-oriented perceptual feedback generated by reactive sub-systems.- The compatibility of the probabilistic BDI model with the affect and motivational models and affective and motivational valences used throughout CernoCAMAL.A succession of experiments in simulation and robotic testbeds is carried out to demonstrate improvements and increased efficacy in CernoCAMAL’s overall cognitive performance. A discussion and critical appraisal of the experimental results, together with a summary, a number of potential future research directions, and some closing remarks conclude the thesis

Proceedings of the 3rd international conference on disability, virtual reality and associated technologies (ICDVRAT 2000)

The proceedings of the conferenc

Exploring design space and niche space

SIGLEGBUnited Kingdo