A perspective on lifelong open-ended learning autonomy for robotics through cognitive architectures

[Abstract]: This paper addresses the problem of achieving lifelong open-ended learning autonomy in robotics, and how different cognitive architectures provide functionalities that support it. To this end, we analyze a set of well-known cognitive architectures in the literature considering the different components they address and how they implement them. Among the main functionalities that are taken as relevant for lifelong open-ended learning autonomy are the fact that architectures must contemplate learning, and the availability of contextual memory systems, motivations or attention. Additionally, we try to establish which of them were actually applied to real robot scenarios. It transpires that in their current form, none of them are completely ready to address this challenge, but some of them do provide some indications on the paths to follow in some of the aspects they contemplate. It can be gleaned that for lifelong open-ended learning autonomy, motivational systems that allow finding domain-dependent goals from general internal drives, contextual long-term memory systems that all allow for associative learning and retrieval of knowledge, and robust learning systems would be the main components required. Nevertheless, other components, such as attention mechanisms or representation management systems, would greatly facilitate operation in complex domains.Ministerio de Ciencia e Innovación; PID2021-126220OB-I00Xunta de Galicia; EDC431C-2021/39Consellería de Cultura, Educación, Formación Profesional e Universidades; ED431G 2019/0

Γνωσιακή μουσικολογία και το υπολογιστικό σύστημα IDyOT

Αρχικά, αναλύονται ζητήματα της έρευνας στη γνωσιακή μουσικολογία, όπως η μουσική ομοιότητα, η νοητικές αναπαραστάσεις της μουσικής, η αντίληψη αλληλουχιών και η μουσική προσδοκία. Γίνεται εισαγωγή στην υπολογιστική δημιουργικότητα, τη θεωρία πληροφορίας, του εννοιολογικούς χώρους και τη γνωσιακή αρχιτεκτονική. Στη συνέχεια, εκτίθεται ενδελεχώς η γνωσιακή αρχιτεκτονική IDyOT, από την επιστημονική προέλευσή της και το χαμηλότερο επίπεδο λειτουργίας των γεννητριών της, μέχρι θέματα αναπαράστασης, μνήμης, πρόβλεψης, μνημονικής παγίωσης αλλά και εφαρμογές της σε τεμαχιοποίηση, συγχρονισμό και δημιουργικότητα. Τέλος, παρουσιάζεται η γνωσιακή αρχιτεκτονική MicroPSI, που μοντελοποιεί την κινητοποίηση και το συναίσθημα και εξερευνάται ένας πιθανός τρόπος σύνδεσής της με το IDyOT.Initially, matters of cognitive musicology research are analyzed, such as musical similarity, musical mental representations, sequence perception and musical expectancy. Computational creativity, information theory, conceptual spaces and cognitive architecture are introduced. Consequently, the IDyOT cognitive architecture is thoroughly presented, from its scientific origins and the lower level of function of its generators, up to matters of representation, memory, prediction, memory consolidation but also its applications in chunking, entrainment and creativity. Finally, the MicroPSI cognitive architecture is presented, which models motivation and affect, and a possibility of connection with IDyOT is explored

Unsupervised Learning of Reflexive and Action-Based Affordances to Model Adaptive Navigational Behavior

Here we introduce a cognitive model capable to model a variety of behavioral domains and apply it to a navigational task. We used place cells as sensory representation, such that the cells’ place fields divided the environment into discrete states. The robot learns knowledge of the environment by memorizing the sensory outcome of its motor actions. This is composed of a central process, learning the probability of state-to-state transitions by motor actions and a distal processing routine, learning the extent to which these state-to-state transitions are caused by sensory-driven reflex behavior (obstacle avoidance). Navigational decision making integrates central and distal learned environmental knowledge to select an action that leads to a goal state. Differentiating distal and central processing increases the behavioral accuracy of the selected actions and the ability of behavioral adaptation to a changed environment. We propose that the system can canonically be expanded to model other behaviors, using alternative definitions of states and actions. The emphasis of this paper is to test this general cognitive model on a robot in a real-world environment

Abel: Integrating Humanoid Body, Emotions, and Time Perception to Investigate Social Interaction and Human Cognition

Humanoids have been created for assisting or replacing humans in many applications, providing encouraging results in contexts where social and emotional interaction is required, such as healthcare, education, and therapy. Bioinspiration, that has often guided the design of their bodies and minds, made them also become excellent research tools, probably the best platform by which we can model, test, and understand the human mind and behavior. Driven by the aim of creating a believable robot for interactive applications, as well as a research platform for investigating human cognition and emotion, we are constructing a new humanoid social robot: Abel. In this paper, we discussed three of the fundamental principles that motivated the design of Abel and its cognitive and emotional system: hyper-realistic humanoid aesthetics, human-inspired emotion processing, and human-like perception of time. After reporting a brief state-of-the-art on the related topics, we present the robot at its stage of development, what are the perspectives for its application, and how it could satisfy the expectations as a tool to investigate the human mind, behavior, and consciousness

Group Cohesion in Multi-Agent Scenarios as an Emergent Behavior

In this paper, we elaborate on the design and discuss the results of a multi-agent simulation that we have developed using the PSI cognitive architecture. We demonstrate that imbuing agents with intrinsic needs for group affiliation, certainty and competence will lead to the emergence of social behavior among agents. This behavior expresses itself in altruism toward in-group agents and adversarial tendencies toward out-group agents. Our simulation also shows how parameterization can have dramatic effects on agent behavior. Introducing an out-group bias, for example, not only made agents behave aggressively toward members of the other group, but it also increased in-group cohesion. Similarly, environmental and situational factors facilitated the emergence of outliers: agents from adversarial groups becoming close friends. Overall, this simulation showcases the power of psychological frameworks, in general, and the PSI paradigm, in particular, to bring about human-like behavioral patterns in an emergent fashion

Building a motivational subsystem for the cognitive system toolkit

Orientador: Ricardo Ribeiro GudwinDissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de ComputaçãoResumo: Motivações e emoções estão inseridas intrinsecamente na cognição e no comportamento de diversos tipos de animais, particularmente nos seres humanos. São responsáveis por apoiar a tomada de decisões, estimulando comportamentos diferentes de modo que suas necessidades internas sejam satisfeitas. Este trabalho propõe a concepção e implementação de um subsistema motivacional dotado de capacidades motivacionais e emocionais para o Cognitive System Toolkit (CST), um conjunto de ferramentas de software para computação cognitiva desenvolvido pelo nosso grupo de pesquisa, com base em estudos da literatura e diferentes implementações de sistemas motivacionais e emocionais em arquiteturas cognitivas conhecidas. Neste contexto, o Subsistema Motivacional foi submetido a um conjunto de simulações no "World Server 3D Application". Neste simulador, é possível realizar simulações onde uma criatura artificial "vive" em um ambiente virtual onde pode encontrar comida, obstáculos e objetos de valor. Sobre esses objetos, a criatura pode realizar um conjunto de ações. No caso das comidas, o agente pode comê-las, guardá-las em sua sacola ou enterrá-las para que possam ser utilizadas posteriormente. Já para as joias, o agente pode capturá-las e guardá-las em sua sacola para que elas sejam trocadas por pontos no ponto de troca (\textit{Delivery Spot}). Elas também podem ser enterradas, para evitar que a criatura colida com joias indesejáveis durante o processo de exploração do ambiente. No contexto dos obstáculos, eles são objetos inanimados que têm por objetivo dificultar a percepção e a exploração da criatura quando ela está em busca de objetos desejáveis. Para que se possa avaliar a eficiência e eficácia do modelo, foi desenvolvido um conjunto de experimentos com diferentes controladores inteligentes utilizando o modelo de subsistema motivacional, um sistema reativo e também um conjunto de arquiteturas cognitivas como: JSOAR, CLARION e LIDA. Os experimentos são executados durante o período de dez minutos e têm por objetivo fazer com que a criatura virtual capture joias presentes no ambiente e troque-as por pontos, ao mesmo tempo garantir que o agente sobreviva aos gastos energéticos oriundos da movimentação e exploração do ambiente. Com a execução dos experimentos, pode-se observar que o controlador motivacional foi o melhor controlador, considerando-se os aspectos de eficiência energética, com uma média igual a "535.36" e mediana igual "530.00". Além disso, com o valor da média dos desvios-padrão de "177.88" e variância de "33331.17", também pode-se afirmar que o controlador motivacional foi o controlador que manteve mais estável as reservas de energia da criatura. Do ponto de vista da eficácia energética, o controlador motivacional obteve uma acurácia de "80%", ou seja, dentre dez experimento executados, oito deles conseguiram manter a energia da criatura acima de zero até fim da simulação. Considerando-se a máxima pontuação obtida, novamente o controlador motivacional foi o melhor entre todos os controladores testados, com uma pontuação média igual a "77.3". Outro fator primordial para máxima obtenção de pontos foi a geração e execução de planos. O experimento em que obtivemos a maior pontuação dentre todos experimentos realizados com o controlador motivacional foi o sexto. Nele, o sistema de planejamento presente no Subsistema Motivacional de Alto Nível gerou e executou três planos e obteve uma pontuação de "135'". Sendo assim, conclui-se que, em relação ao controle de sistemas bio-inspirados, o controlador motivacional se mostrou superior, quando comparado aos demais controladores testadosAbstract: Motivations and emotions are intrinsically embedded in animal cognition and behavior, particularly in humans. They are responsible for supporting decision making, stimulating different behaviors such that their internal needs are satisfied. This work proposes the design and implementation of a motivational subsystem endowed with motivational and emotional capacities for the Cognitive System Toolkit (CST), a software toolkit for cognitive computing being developed by our research group, based on studies from the literature and different implementations of motivational and emotional systems in known cognitive architectures. In this context, the Motivational Subsystem was submitted to a set of simulations in the World Server 3D Application. In this simulator, it is possible to perform simulations where an artificial creature "lives" in a virtual environment where it can find food, obstacles and objects of value. Upon these objects, the creature can perform a set of actions. In the case of food, the agent can eat them, store them in their bag or bury them, such that they can be used later. For jewels, the agent can capture them and store them in their bag such that they are exchanged for points at the (\textit{Delivery Spot}). They can also be buried to prevent the creature from colliding with undesirable jewels during the process of exploring the environment. In the context of obstacles, they are inanimate objects where they aim to make the creature's perception and exploitation difficult when it is in search of desirable objects. In order to evaluate the efficiency and effectiveness of the model, we developed a set of experiments with different intelligent controllers using the motivational subsystem model, a reactive system and also a set of cognitive architectures such as: JSOAR, CLARION and LIDA. The experiments are executed during a ten-minute period and are intended to cause the virtual creature to capture jewelry present in the environment, exchange them for points and at the same time make the agent survive, considering the energy expenditure demanded by movement while exploring the environment. With the execution of the experiments, we observed that the motivational controller was the best controller, regarding energy efficiency, with a mean equals to "535.36" and a median equals to "530.00". In addition, with the mean value of the standard deviation of "177.88" and variance of "33331.17", we noticed that the motivational controller was the controller which kept the creature's energy more stable. From the point of view of energy efficiency, the motivational controller obtained "80%" accuracy of its experiments, that is, among ten performed experiments, eight of them resulted in keeping the creature's energy above zero until the end of the simulation . Considering the maximum score obtained, again the motivational controller was the best among all controllers with an average score of "77.3". Another factor that was paramount for maximum points achievement was the generation and execution of plans. The experiment obtaining the highest score among all experiments with the motivational controller was the sixth one. In it, the planning system present in the High-Level Motivational Subsystem generated and executed three plans and got a score of "135". Thus, we conclude that the motivational controller, when compared to the other controllers we tested was superior in what concerns the control of bio-inspired systemsMestradoEngenharia de ComputaçãoMestre em Engenharia Elétric

A Universal Knowledge Model and Cognitive Architecture for Prototyping AGI

The article identified 42 cognitive architectures for creating general artificial intelligence (AGI) and proposed a set of interrelated functional blocks that an agent approaching AGI in its capabilities should possess. Since the required set of blocks is not found in any of the existing architectures, the article proposes a new cognitive architecture for intelligent systems approaching AGI in their capabilities. As one of the key solutions within the framework of the architecture, a universal method of knowledge representation is proposed, which allows combining various non-formalized, partially and fully formalized methods of knowledge representation in a single knowledge base, such as texts in natural languages, images, audio and video recordings, graphs, algorithms, databases, neural networks, knowledge graphs, ontologies, frames, essence-property-relation models, production systems, predicate calculus models, conceptual models, and others. To combine and structure various fragments of knowledge, archigraph models are used, constructed as a development of annotated metagraphs. As components, the cognitive architecture being developed includes machine consciousness, machine subconsciousness, blocks of interaction with the external environment, a goal management block, an emotional control system, a block of social interaction, a block of reflection, an ethics block and a worldview block, a learning block, a monitoring block, blocks of statement and solving problems, self-organization and meta learning block

Normative Emotional Agents: a viewpoint paper

[EN] Human social relationships imply conforming to the norms, behaviors and cultural values of the society, but also socialization of emotions, to learn how to interpret and show them. In multiagent systems, much progress has been made in the analysis and interpretation of both emotions and norms. Nonetheless, the relationship between emotions and norms has hardly been considered and most normative agents do not consider emotions, or vice-versa. In this article, we provide an overview of relevant aspects within the area of normative agents and emotional agents. First we focus on the concept of norm, the different types of norms, its life cycle and a review of multiagent normative systems. Secondly, we present the most relevant theories of emotions, the life cycle of an agent¿s emotions, and how emotions have been included through computational models in multiagent systems. Next, we present an analysis of proposals that integrate emotions and norms in multiagent systems. From this analysis, four relationships are detected between norms and emotions, which we analyze in detail and discuss how these relationships have been tackled in the reviewed proposals. Finally, we present a proposal for an abstract architecture of a Normative Emotional Agent that covers these four norm-emotion relationships.This work was supported by the Spanish Government project TIN2017-89156- R, the Generalitat Valenciana project PROMETEO/2018/002 and the Spanish Goverment PhD Grant PRE2018-084940.Argente, E.; Del Val, E.; Pérez-García, D.; Botti Navarro, VJ. (2022). Normative Emotional Agents: a viewpoint paper. IEEE Transactions on Affective Computing. 13(3):1254-1273. https://doi.org/10.1109/TAFFC.2020.3028512S1254127313