Artificial Cognitive Architecture Research Articles

Comparing LLMs for Prompt-Enhanced ACT-R and Soar Model Development: A Case Study in Cognitive Simulation

This paper presents experiments on using ChatGPT4 and Google Bard to create ACT-R and Soar models. The study involves two simulated cognitive tasks, where ChatGPT4 and Google Bard (Large Language Models, LLMs) serve as conversational interfaces within the ACT-R and Soar framework development environments. The first task involves creating an intelligent driving model using ACT-R with motor and perceptual behavior and can further interact with an unmodified interface. The second task evaluates the development of educational skills using Soar. Prompts were designed to represent cognitive operations and actions, including providing context, asking perception-related questions, decision-making scenarios, and evaluating the system's responses, and they were iteratively refined based on model behavior evaluation. Results demonstrate the potential of using LLMs to serve as interactive interfaces to develop ACT-R and Soar models within a human-in-the-loop model development process. We documented the mistakes LLMs made during this integration and provided corresponding resolutions when adopting this modeling approach. Furthermore, we presented a framework of prompt patterns that maximizes LLMs interaction for artificial cognitive architectures.

Exploring the Use of Multiagent Systems in Educational Robotics Activities

Multi-Agent Systems (MAS) have been demonstrated to be a useful tool for addressing complex problems by breaking them down into simpler tasks. In this paper, we explore the potential of the MAS approach in designing educational robotics learning experiences. Specifically, we aim to identify the pedagogical possibilities and technical considerations of using MAS in designing robot-based activities. We have designed three educational robotics experiences based on the MAS approach. The first experience describes the use of the MAS approach in creating a learning activity based on Multi-Robots Systems (MRS). The second experience describes the use of the MAS approach for implementing theater-based activities with MRS. Finally, the third experience outlines the implementation of an artificial cognitive architecture based on MAS to control the educational actions of an anthropomorphic social robot. The results reveal that MAS and MRS offer opportunities to design distributed learning environments with distributed artificial intelligence.

Perception, Imagery, Memory and Consciousness

I propose and discuss some principles that I believe are substantial for perception, various kinds of memory, expectations and the capacity for imagination in the mammal brain, as well as for the design of a biologically inspired artificial cognitive architecture. I also suggest why these same principles could explain our ability to represent novel concepts and imagine non-existing and perhaps impossible objects, while there are still limits to what we can imagine and think about. Some ideas regarding how these principles could be relevant for an autonomous agent to become functionally conscious are discussed as well.

The collaborative mind: intention reading and trust in human-robot interaction

SummaryRobots are likely to become important social actors in our future and so require more human-like ways of assisting us. We state that collaboration between humans and robots is fostered by two cognitive skills: intention reading and trust. An agent possessing these abilities would be able to infer the non-verbal intentions of others and to evaluate how likely they are to achieve their goals, jointly understanding what kind and which degree of collaboration they require. For this reason, we propose a developmental artificial cognitive architecture that integrates unsupervised machine learning and probabilistic models to imbue a humanoid robot with intention reading and trusting capabilities. Our experimental results show that the synergistic implementation of these cognitive skills enable the robot to cooperate in a meaningful way, with the intention reading model allowing a correct goal prediction and with the trust component enhancing the likelihood of a positive outcome for the task.

Artificial Cognitive Architectures Review

In this work we present the review of cognitive architectures and bio-inspired approaches used for cognitive modeling with focus on consciousness and common sense computational implementation.

Would a robot trust you? Developmental robotics model of trust and theory of mind.

Trust is a critical issue in human–robot interactions: as robotic systems gain complexity, it becomes crucial for them to be able to blend into our society by maximizing their acceptability and reliability. Various studies have examined how trust is attributed by people to robots, but fewer have investigated the opposite scenario, where a robot is the trustor and a human is the trustee. The ability for an agent to evaluate the trustworthiness of its sources of information is particularly useful in joint task situations where people and robots must collaborate to reach shared goals. We propose an artificial cognitive architecture based on the developmental robotics paradigm that can estimate the trustworthiness of its human interactors for the purpose of decision making. This is accomplished using Theory of Mind (ToM), the psychological ability to assign to others beliefs and intentions that can differ from one’s owns. Our work is focused on a humanoid robot cognitive architecture that integrates a probabilistic ToM and trust model supported by an episodic memory system. We tested our architecture on an established developmental psychological experiment, achieving the same results obtained by children, thus demonstrating a new method to enhance the quality of human and robot collaborations.This article is part of the theme issue ‘From social brains to social robots: applying neurocognitive insights to human–robot interaction’.

Designing Artificial Cognitive Architectures: Brain Inspired or Biologically Inspired?

Artificial Neural Networks (ANNs) were devised as a tool for Artificial Intelligence (AI) design implementations. However, it was soon became obvious that they are unable to fulfil their duties. The fully autonomous way of ANNs working, precluded from any human intervention or supervision, deprived of any theoretical underpinning, leads to a strange state of affairs, when ANN designers cannot explain why and how they achieve their amazing and remarkable results. Therefore, contemporary Artificial Intelligence R&D looks more like a Modern Alchemy enterprise rather than a respected scientific or technological undertaking. On the other hand, modern biological science posits that intelligence can be distinguished not only in human brains. Intelligence today is considered as a fundamental property of each and every living being. Therefore, lower simplified forms of natural intelligence are more suitable for investigation and further replication in artificial cognitive architectures.

Integration of Action, Joint Action and Learning in Robot Cognitive Architectures

Intégration de l’action, de l’action conjointe et de l’apprentissage dans les architectures cognitives robotiques. À l’opposé des recherches en Intelligence Artificielle, où des algorithmes conçus pour des problèmes spécifiques peuvent être testés dans des conditions de simulation parfaitement contrôlée, la Robotique a toujours eu à faire face à la nécessité d’intégrer la perception, la décision et l’exécution de l’action pour pouvoir fonctionner sur des plateformes physiques en interaction dans et avec le monde réel. Cette particularité a obligé les roboticiennes et roboticiens à se poser très tôt la question de comment coordonner efficacement différents systèmes de mémoires, différents niveaux de prise de décision et différents processus d’apprentissage dans des architectures cognitives. En conséquence, les propositions d’architectures robotiques ont souvent été confrontées et nourries par les questionnements sur les architectures cognitives tels qu’abordés en Philosophie, en Psychologie, en Neurosciences, et plus généralement en Sciences Cognitives. Dans cet article, nous passons en revue les travaux robotiques qui ont abordé le problème de l’intégration de différents niveaux d’action et de leurs mécanismes d’apprentissage associés (de la planification des actions orientées vers un but aux simples réflexes ; de la surveillance de l’action aux comportements réactifs guidés par la vision). Nous montrons qu’un tel type d’intégration est nécessaire et suffisant pour permettre à un agent artificiel de montrer un niveau basique de surveillance de sa propre performance, et en conséquence de montrer de plus grandes capacités de flexibilité comportementale, d’autonomie et de généralisation à différents environnements. Nous illustrons cette problématique à travers des exemples expérimentaux sur la coordination de systèmes multiples d’apprentissage au sein d’un même robot, et sur l’application à l’action conjointe lors de la coopération homme-robot. Nous trouvons en effet que des mécanismes partiellement similaires d’intégration des niveaux d’action peuvent fonctionner à la fois pour l’action individuelle et pour l’action conjointe. Enfin, nous mettons en lumière certains des succès et des échecs de ces approches robotiques en espérant nourrir et contribuer aux débats similaires sur l’action qui se posent dans les autres champs des Sciences Cognitives.

Artificial cognitive control with self-x capabilities: A case study of a micro-manufacturing process

Nowadays, even though cognitive control architectures form an important area of research, there are many constraints on the broad application of cognitive control at an industrial level and very few systematic approaches truly inspired by biological processes, from the perspective of control engineering. Thus, our main purpose here is the emulation of human socio-cognitive skills, so as to approach control engineering problems in an effective way at an industrial level. The artificial cognitive control architecture that we propose, based on the shared circuits model of socio-cognitive skills, seeks to overcome limitations from the perspectives of computer science, neuroscience and systems engineering. The design and implementation of artificial cognitive control architecture is focused on four key areas: (i) self-optimization and self-leaning capabilities by estimation of distribution and reinforcement-learning mechanisms; (ii) portability and scalability based on low-cost computing platforms; (iii) connectivity based on middleware; and (iv) model-driven approaches. The results of simulation and real-time application to force control of micro-manufacturing processes are presented as a proof of concept. The proof of concept of force control yields good transient responses, short settling times and acceptable steady-state error. The artificial cognitive control architecture built into a low-cost computing platform demonstrates the suitability of its implementation in an industrial setup.

Yes and No: Match/Mismatch Function in Cognitive Robots

Associative match, mismatch and novelty detection is an essential function in human and artificial cognition and is related to perception, reasoning and emotion. In the human brain, match/mismatch-related and novelty-detection-related signals and states can be detected by EEG and more advanced imaging technologies. Match, mismatch and novelty detection should also be present in artificial cognitive architectures that seek to provide a framework for artificial cognition, especially for autonomous robots. Here, the functions of match, mismatch and novelty are discussed, their implementation in one artificial cognitive architecture (HCA) is described and their application in the robot XCR-1 is given as a practical example.

INNER SPEECH GENERATION IN A VIDEO GAME NON-PLAYER CHARACTER: FROM EXPLANATION TO SELF?

The use of human language is a hallmark of human consciousness, even when it is not used publicly. Inner speech is the way humans consciously communicate with themselves and arguably a key factor contributing to the formation of more self-aware selves. From the perspective of cognitive science and artificial cognitive architectures, inner speech can be also seen as a meta-management system that modulates some cognitive processes of the subject. In this paper, we describe a preliminary version of a computational model of inner speech generation based on the cognitive architecture CERA-CRANIUM. This inner speech generation method is illustrated using a video game non-player character as the subject of the first-person narratives to be produced. We also use this model of inner speech generation to discuss the possibilities of using such a first-person narrative stream as a meta-control input to the artificial cognitive architecture. We argue that this verbal input might be used as an integrated self-explanation of the agent in the world and thus contribute to the formation of self.

Modelling Artificial Cognition in Biosemiotic Terms

Stemming from Uexkull’s fundamental concepts of Umwelt and Innenwelt as developed in the biosemiotic approach of Ferreira 2010, 2011, the present work models mathematically the semiosis of cognition and proposes an artificial cognitive architecture to be deployed in a robotic structure.

Exploratory learning structures in artificial cognitive systems

The major goal of the COSPAL project is to develop an artificial cognitive system architecture, with the ability to autonomously extend its capabilities. Exploratory learning is one strategy that allows an extension of competences as provided by the environment of the system. Whereas classical learning methods aim at best for a parametric generalization, i.e., concluding from a number of samples of a problem class to the problem class itself, exploration aims at applying acquired competences to a new problem class, and to apply generalization on a conceptual level, resulting in new models. Incremental or online learning is a crucial requirement to perform exploratory learning. In the COSPAL project, we mainly investigate reinforcement-type learning methods for exploratory learning, and in this paper we focus on the organization of cognitive systems for efficient operation. Learning is used over the entire system. It is organized in the form of four nested loops, where the outermost loop reflects the user-reinforcement-feedback loop, the intermediate two loops switch between different solution modes at symbolic respectively sub-symbolic level, and the innermost loop performs the acquired competences in terms of perception–action cycles. We present a system diagram which explains this process in more detail. We discuss the learning strategy in terms of learning scenarios provided by the user. This interaction between user (’teacher’) and system is a major difference to classical robotics systems, where the system designer places his world model into the system. We believe that this is the key to extendable robust system behavior and successful interaction of humans and artificial cognitive systems. We furthermore address the issue of bootstrapping the system, and, in particular, the visual recognition module. We give some more in-depth details about our recognition method and how feedback from higher levels is implemented. The described system is however work in progress and no final results are available yet. The available preliminary results that we have achieved so far, clearly point towards a successful proof of the architecture concept.