Cognitive Systems Research最新文献

The theory of Enactive Inference was proposed by Karl Friston and his colleagues to explain how the brain infers knowledge about the world through the subject’s interactive experiences. Sensorimotor states induce perturbations in neural activity, and the brain infers hypothetical causes in the world that may explain these perturbations. This article aims to reconcile this neuroscience theory with computer science and artificial-intelligence theories wherein artificial agents receive input data derived from the environment’s state and infer internal data structures used to guide decisions. Two critical challenges arise in both the agent’s active role and the inference algorithm’s scalability as the environment’s complexity increases. To address these challenges, we formalize artificial enactive inference through a new Spatial Enactive Markov Decision Process (SEMDP) model. This model rests on low-level control loops enacted in a three-dimensional Euclidean space containing objects. Based on the SEMDP, we present a proof-of-concept cognitive architecture and an experiment to demonstrate the transcription of the theory of enactive inference into the domain of artificial intelligence and robotics.

This article presents a comprehensive analysis of the different tests proposed in the recent ChildCI framework,¹ proving its potential for generating a better understanding of children’s neuromotor and cognitive development along time, as well as their possible application in other research areas such as e-Health and e-Learning. In particular, we propose a set of over 100 global features related to motor and cognitive aspects of the children interaction with mobile devices, some of them collected and adapted from the literature.

Furthermore, we analyse the robustness and discriminative power of the proposed feature set including experimental results for the task of children age group detection based on their motor and cognitive behaviours. Two different scenarios are considered in this study: (i) single-test scenario, and (ii) multiple-test scenario. Results over 93% accuracy are achieved using the publicly available ChildCIdb_v1 database (over 400 children from 18 months to 8 years old), proving the high correlation of children’s age with the way they interact with mobile devices.

Whenever someone in a team tries to help others, it is crucial that they have some understanding of other team members’ goals. In modern teams, this applies equally to human and artificial (“bot”) assistants. Understanding when one does not know something is crucial for stopping the execution of inappropriate behavior and, ideally, attempting to learn more appropriate actions. From a statistical point of view, this can be translated to assessing whether none of the hypotheses in a considered set is correct. Here we investigate a novel approach for making this assessment based on monitoring the maximum a posteriori probability (MAP) of a set of candidate hypotheses as new observations arrive. Simulation studies suggest that this is a promising approach, however, we also caution that there may be cases where this is more challenging. The problem we study and the solution we propose are general, with applications well beyond human–bot teaming, including for example the scientific process of theory development.

This article presents the use of second-order adaptive network models of hospital teams consisting of doctors and nurses, interacting together. A variety of scenarios are modelled and simulated, in relation with respiratory distress of a neonate, along with the integration of an AI-Coach for monitoring and support of such teams and of organizational learning. The research highlights the benefits of introducing a virtual AI-Coach in a hospital setting. The practical application setting revolves around a medical team responsible for managing neonates with respiratory distress. In this setting an AI-Coach act as an additional team member, to ensure correct execution of medical procedure. Through simulation experiments, the adaptive network models demonstrate that the AI-Coach not only aids in maintaining correct medical procedure execution but also facilitates organizational learning, leading to significant improvements in procedure adherence and error reduction during neonatal care.

The integration of robotics into everyday life is increasing and these complex systems are exposed to complex faults that require rapid identification for seamless repair and continuous operation. These faults have a complex impact on cognitive aspects such as perception, decision-making and behavioral execution in robots. Robotic fault detection and diagnosis research (FDD) focuses primarily on individual robot scenarios, which lack a comprehensive investigation in multi-robot systems (MRSs). Our paper introduces a robotic control method to control operations in a wide range of production systems. The control system architecture developed by multiple robots provides a local and global cognitive system that is shared between them. Internal dynamics, represented by finite state machines, represent different operating scenarios. The rigorous formal methodology such as Petri Nets and Computer Tree Logic (CTL) validates the accuracy of control architectures and fault management strategies. Building a model of trust based on the historical interactions between intelligent robots facilitates the creation of a global cognitive system that enables adaptation in the management of errors. Our research is launching a trust estimation model, especially the collaboration between reliable robots, and increasing the fault flexibility of multirobot control systems. The contributions include the design of multi-robot control architectures, the management of failures of control robots, and the formulation of trust models.

Compositionality can be considered as finding (or creating) the correct meaning of the constituents of a non-simple language expression or visual image. The Causal Cognitive Architecture is a brain-inspired cognitive architecture (BICA). It is not a traditional artificial neural network architecture, nor a traditional symbolic AI system but instead uses spatial navigation maps as its fundamental circuits. In previously described versions of the architecture, sensory inputs are compared in each existing sensory system against previous stored navigation maps for that sensory system, and the best navigation map is chosen and then updated with the new sensory inputs and a best multisensory navigation map is similarly created and used as the working navigation map. Instinctive and learned small procedures are triggered by input sensory inputs as well as matched navigation maps, and in the Navigation Module operate on the working navigation map and produce an output signal. By feeding back intermediate results in the Navigation Module it has been shown previously how causal and analogical behaviors emerge from the architecture. In new work, the Navigation Module is duplicated in a biologically plausible manner. It becomes possible to compositionally process information in the duplicated Navigation Module, and as a result compositional language comprehension and behavior readily emerge. A formalization and simulation of the architecture is presented. A demonstration example, and its negation, are explored of solving a compositional problem requiring the placement of an object in a specific location with regard to other objects. Future work is discussed using large language models to create navigation maps. Given the mammalian brain inspiration of the architecture, it suggests that it is indeed feasible for modest genetic changes to have allowed the emergence of compositional language in humans.