Time-Aware Multi-Agent Symbiosis.

Contemporary research in human-machine symbiosis has mainly concentrated on enhancing relevant sensory, perceptual, and motor capacities, assuming short-term and nearly momentary interaction sessions. Still, human-machine confluence encompasses an inherent temporal dimension that is typically overlooked. The present work shifts the focus on the temporal and long-lasting aspects of symbiotic human-robot interaction (sHRI). We explore the integration of three time-aware modules, each one focusing on a diverse part of the sHRI timeline. Specifically, the Episodic Memory considers past experiences, the Generative Time Models estimate the progress of ongoing activities, and the Daisy Planner devices plans for the timely accomplishment of goals. The integrated system is employed to coordinate the activities of a multi-agent team. Accordingly, the proposed system (i) predicts human preferences based on past experience, (ii) estimates performance profile and task completion time, by monitoring human activity, and (iii) dynamically adapts multi-agent activity plans to changes in expectation and Human-Robot Interaction (HRI) performance. The system is deployed and extensively assessed in real-world and simulated environments. The obtained results suggest that building upon the unfolding and the temporal properties of team tasks can significantly enhance the fluency of sHRI.

The explicit judgment of long durations of several minutes in everyday life: Conscious retrospective memory judgment and the role of affects?

In this study, individuals estimated interval times of several minutes (from 2 to 32 minutes) during their everyday lives using a cell phone they kept with them. Their emotional state, the difficulty of the activity performed during this interval, and the attention that it required were also assessed, together with their subjective experience of the passage of time. The results showed that the mean time estimates and their variability increased linearly with increasing interval duration, indicating that the fundamental scalar property of time found for short durations also applies to very long durations of several minutes. In addition, the emotional state and difficulty of the activity were significant predictors of the judgment of long durations. However, the awareness of the passage of time appeared to play a crucial role in the judgment of very long duration in humans. A theory on the emergence of the awareness of the passage of time and how it affects the judgment of interval durations lasting several minutes is therefore discussed.

Passage of time judgments in everyday life are not related to duration judgments except for long durations of several minutes.

This study investigated relations between judgments of passage of time and judgments of long durations in everyday life with an experience sampling method. Several times per day, the participants received an alert via mobile phone. On each alert, at the same time as reporting their experience of the passage of time, the participants also estimated durations, between 3 and 33s in Experiment 1, and between 2 and 8min in Experiment 2. The participants' affective states and the difficulty and attentional demands of their current activity were also assessed. The results replicated others showing that affective states and the focus of attention on current activity are significant predictors of individual differences in passage-of-time judgments. In addition, the passage-of-time judgments were significantly related to the duration judgments but only for long durations of several minutes.

When and How-Long: A Unified Approach for Time Perception.

The representation of the environment assumes the encoding of four basic dimensions in the brain, that is the 3D space and time. The vital role of time for cognition is a topic that recently attracted increasing research interest. Surprisingly, the scientific community investigating mind-time interactions has mainly focused on interval timing, paying less attention on the encoding and processing of distant moments. The present work highlights two basic capacities that are necessary for developing temporal cognition in artificial systems. In particular, the seamless integration of agents in the environment assumes they are able to consider when events have occurred and how-long they have lasted. This information, although rather standard in humans, is largely missing from artificial cognitive systems. In this work we consider how a time perception model that is based on neural networks and the Striatal Beat Frequency (SBF) theory is extended in a way that besides the duration of events, facilitates the encoding of the time of occurrence in memory. The extended model is capable to support skills assumed in temporal cognition and answer time-related questions about the unfolded events.

Time models and cognitive processes: a review.

The sense of time is an essential capacity of humans, with a major role in many of the cognitive processes expressed in our daily lifes. So far, in cognitive science and robotics research, mental capacities have been investigated in a theoretical and modeling framework that largely neglects the flow of time. Only recently there has been a rather limited, but constantly increasing interest in the temporal aspects of cognition, integrating time into a range of different models of perceptuo-motor capacities. The current paper aims to review existing works in the field and suggest directions for fruitful future work. This is particularly important for the newly developed field of artificial temporal cognition that is expected to significantly contribute in the development of sophisticated artificial agents seamlessly integrated into human societies.

Self-organizing high-order cognitive functions in artificial agents: implications for possible prefrontal cortex mechanisms.

In our daily life, we often adapt plans and behaviors according to dynamically changing world circumstances, selecting activities that make us feel more confident about the future. In this adaptation, the prefrontal cortex (PFC) is believed to have an important role, applying executive control on other cognitive processes to achieve context switching and confidence monitoring; however, many questions remain open regarding the nature of neural processes supporting executive control. The current work explores possible mechanisms of this high-order cognitive function, transferring executing control in the domain of artificial cognitive systems. In particular, we study the self-organization of artificial neural networks accomplishing a robotic rule-switching task analogous to the Wisconsin Card Sorting Test. The obtained results show that behavioral rules may be encoded in neuro-dynamic attractors, with their geometric arrangements in phase space affecting the shaping of confidence. Analysis of the emergent dynamical structures suggests possible explanations of the interactions of high-level and low-level processes in the real brain.

Temporal cognition: a key ingredient of intelligent systems.

Experiencing the flow of time is an important capacity of biological systems that is involved in many ways in the daily activities of humans and animals. However, in the field of robotics, the key role of time in cognition is not adequately considered in contemporary research, with artificial agents focusing mainly on the spatial extent of sensory information, almost always neglecting its temporal dimension. This fact significantly obstructs the development of high-level robotic cognitive skills, as well as the autonomous and seamless operation of artificial agents in human environments. Taking inspiration from biological cognition, the present work puts forward time perception as a vital capacity of artificial intelligent systems and contemplates the research path for incorporating temporal cognition in the repertoire of robotic skills.

Explorations on artificial time perception.

In the field of biologically inspired cognitive systems, time perception, a fundamental aspect of natural cognition is not sufficiently explored. The majority of existing works ignore the importance of experiencing the flow of time, and the implemented agents are rarely furnished with time processing capacities. The current work aims at shedding light on this largely unexplored issue, focusing on the perception of temporal duration. Specifically, we investigate a rule switching task that consists of repeating trials with dynamic temporal lengths. An evolutionary process is employed to search for neuronal mechanisms that accomplish the underlying task and self-organize time-processing dynamics. Our repeated simulation experiments showed that the capacity of perceiving duration biases the functionality of neural mechanisms with other cognitive responsibilities and additionally that time perception and ordinary cognitive processes may share the same neural resources in the cognitive system. The obtained results are related with previous brain imaging studies on time perception, and they are used to formulate suggestions for the cortical representation of time in biological agents.

Agent-based brain modeling by means of hierarchical cooperative coevolution.

We address the development of brain-inspired models that will be embedded in robotic systems to support their cognitive abilities. We introduce a novel agent-based coevolutionary computational framework for modeling assemblies of brain areas. Specifically, self-organized agent structures are employed to represent brain areas. In order to support the design of agents, we introduce a hierarchical cooperative coevolutionary (HCCE) scheme that effectively specifies the structural details of autonomous, yet cooperating system components. The design process is facilitated by the capability of the HCCE-based design mechanism to investigate the performance of the model in lesion conditions. Interestingly, HCCE also provides a consistent mechanism to reconfigure (if necessary) the structure of agents, facilitating follow-up modeling efforts. Implemented models are embedded in a simulated robot to support its behavioral capabilities, also demonstrating the validity of the proposed computational framework.

Modelling brain emergent behaviours through coevolution of neural agents.

Recently, many research efforts focus on modelling partial brain areas with the long-term goal to support cognitive abilities of artificial organisms. Existing models usually suffer from heterogeneity, which constitutes their integration very difficult. The present work introduces a computational framework to address brain modelling tasks, emphasizing on the integrative performance of substructures. Moreover, implemented models are embedded in a robotic platform to support its behavioural capabilities. We follow an agent-based approach in the design of substructures to support the autonomy of partial brain structures. Agents are formulated to allow the emergence of a desired behaviour after a certain amount of interaction with the environment. An appropriate collaborative coevolutionary algorithm, able to emphasize both the speciality of brain areas and their cooperative performance, is employed to support design specification of agent structures. The effectiveness of the proposed approach is illustrated through the implementation of computational models for motor cortex and hippocampus, which are successfully tested on a simulated mobile robot.