Cognitive Agent Model Research Articles

A Novel Artificial Social Cognitive Agent Model for Teaching the Concepts

Nowadays, extracting the most suitable information becomes a very challenging problem as accumulation of data over Internet is exponentially increasing. This raises the need of such systems which can process data meaningfully, have capability of understanding the user’s questions, and can provide the most relevant information. The existing systems are not capable of giving precise results. The performance of such systems can be improved using Natural Language Processing (NLP) and Cognitive Computing System. Therefore, an artificial cognitive agent is proposed in this paper using NLP, which can take decisions in real-life using social cognition. These agents are expert in their respective domains. Moreover, social behavioural aspects and the constraints of proposed cognitive agent are also modelled in this work. The performance of the proposed agent is evaluated on the concepts of Electric Motor domain in terms of various parameters. The results depict that the proposed artificial cognitive agent obtains satisfactory results.

A Cognitive Agent Model of Burnout for Front-line Healthcare Professionals in Times of COVID-19 Pandemic

A Cognitive Agent Model of Burnout for Front-line Healthcare Professionals in Times of COVID-19 Pandemic

Enhanced resource allocation in mobile edge computing using reinforcement learning based MOACO algorithm for IIOT

The Mobile networks deploy and offers a multiaspective approach for various resource allocation paradigms and the service based options in the computing segments with its implication in the Industrial Internet of Things (IIOT) and the virtual reality. The Mobile edge computing (MEC) paradigm runs the virtual source with the edge communication between data terminals and the execution in the core network with a high pressure load. The demand to meet all the customer requirements is a better way for planning the execution with the support of cognitive agent. The user data with its behavioral approach is clubbed together to fulfill the service type for IIOT. The swarm intelligence based and reinforcement learning techniques provide a neural caching for the memory within the task execution, the prediction provides the caching strategy and cache business that delay the execution. The factors affecting this delay are predicted with mobile edge computing resources and to assess the performance in the neighboring user equipment. The effectiveness builds a cognitive agent model to assess the resource allocation and the communication network is established to enhance the quality of service. The Reinforcement Learning techniques Multi Objective Ant Colony Optimization (MOACO) algorithms has been applied to deal with the accurate resource allocation between the end users in the way of creating the cost mapping tables creations and optimal allocation in MEC.

Rapid prototyping of cognitive agent simulations using C-BML transformations

Simulating battle management is an essential technique used in planning and mission rehearsal as well as training. Simulation development costs tend to be high due to the complexity of cognitive system architectures in such applications. Due to this complexity, it takes significant effort for a simulation developer to comprehend the problem domain enough to capture accurately in a simulation code. Domain-specific languages (DSL) play an important role in narrowing the communication gap between the domain user and the developer and hence facilitate rapid development. In command and control (C2) applications, the coalition battle management language (C-BML) serves as a DSL for exchanging battle information among C2 systems, simulations, and autonomous elements. In this article, we use a rapid prototyping framework for cognitive agents and demonstrate deployment of agent systems by adopting the model driven engineering approach. To this end, we extend the use of C-BML and automatically transform it in a cognitive agent model, which is then used for adaptive decision making at runtime. As a result, during a simulation run, it is possible to initialize and modify an agent’s goal reasoning model. The cognitive agent models are based on the deliberative coherence theory, which provides a goal reasoning system in terms of coherence-driven agents.

Analyzing airport security checkpoint performance using cognitive agent models

Modern airports operate under high demands and pressures, and strive to satisfy many diverse, interrelated, sometimes conflicting performance goals. Airport performance areas, such as security, safety, and efficiency are usually studied separately from each other. However, operational decisions made by airport managers often impact several areas simultaneously. Current knowledge on how different performance areas are related to each other is limited. This paper contributes to filling this gap by identifying and quantifying relations and trade-offs between the detection performance of illegal items and the average queuing time at airport security checkpoints. These relations and trade-offs were analyzed by simulations with a cognitive agent model of airport security checkpoint operations. By simulation analysis a security checkpoint performance curve with three different regions was identified. Furthermore, the importance of focus on accuracy for a security operator is shown. The results of the simulation studies were related to empirical research at an existing regional airport.

‘Models of’ versus ‘Models for’

The inclusion of the practice of “developing and using models” in the Framework for K-12 Science Education and in the Next Generation Science Standards provides an opportunity for educators to examine the role this practice plays in science and how it can be leveraged in a science classroom. Drawing on conceptions of models in the philosophy of science, we bring forward an agent-based account of models and discuss the implications of this view for enacting modeling in science classrooms. Models, according to this account, can only be understood with respect to the aims and intentions of a cognitive agent (models for), not solely in terms of how they represent phenomena in the world (models of). We present this contrast as a heuristic—models of versus models for—that can be used to help educators notice and interpret how models are positioned in standards, curriculum, and classrooms.

Effects of Simulation Parameters on Naïve Creatures Learning to Safely Cross a Highway on Bimodal Threshold Nature of Success

A model of simulated cognitive agents (naïve creatures) learning to safely cross a cellular automaton based highway is described. These creatures have ability to learn from each other. We investigate how the creatures’ learning outcomes are affected by the model parameters (e.g., the traffic density, creatures’ ability to change a crossing point, creatures’ fear and desire). We observe and study a bimodal nature in the number of successful creatures in various creature populations at simulation end. This value is either low or high depending on the values of the model parameters.

Exploration of Simulated Creatures Learning to Cross a Highway Using Frequency Histograms

We study via frequency histograms, the behaviour of a model of simulated cognitive agents (creatures) learning to safely cross a cellular automaton based highway. The creatures have the ability to learn from each other by evaluating how successful other creatures in the past were for their current situation. We examine the effects of the model parameters on the learning outcomes measured through metrics such as the number of creatures that have successfully crossed. In particular, we focus on the effects of the knowledge base transfer on the creatures’ success of learning. The presented model is general enough so that the considered cognitive agent, called creature, maybe even interpreted as an abstraction of an autonomous vehicle (AV), encountering suddenly another moving vehicle on its trajectory. The AV has to decide whether to continue or to break/stop in order to avoid being destroyed.

Modelling of situation awareness with perception, attention, and prior and retrospective awareness

Abstract Human awareness under different circumstances is complex and non-trivial to understand. Nevertheless, due to the importance of awareness for safety and efficiency in many domains (e.g., the aviation domain), it is necessary to study the processes behind situation awareness, to eliminate possible errors in action selection that may lead to disasters. Interesting models for situation awareness have been presented, mainly from an ecological psychology perspective, but they are debatable with respect to the latest neurocognitive evidences. With the developments in brain imaging and recording techniques, more and more detailed information on complex cognitive processes becomes available. This provides room to further investigate the mechanisms behind many cognitive phenomena, including situation awareness. This paper presents a computational cognitive agent model for situation awareness from the perspective of action selection, which is inspired by neurocognitive evidences. The model integrates bottom-up and top-down cognitive processes, related to various cognitive states: perception, desires, attention, intention, (prior and retrospective) awareness, ownership, feeling, and communication. Based on the model, various cognitive effects can be explained, such as perceptual load, predictive processes, inferential processes, cognitive controlling, unconscious bias, and conscious bias. A model like this will be useful in domains that benefit from complex simulations of socio-technical systems (e.g. the aviation domain) based on computational models of human behaviour. In such domains, existing agent-based simulations are limited, since most of the agent models do not include realistic nature-inspired processes. The validity of the model is illustrated based on simulations for the aviation domain, focusing on a particular situation where an agent has biased perception, poor comprehension, habitual driven projection, and conflict between prior and retrospective effects on action execution.

Naïve Creature with Fear and Desire Learning to Cross a Highway

We describe a model of simple cognitive agent (naïve creature) learning to cross a highway. The naïve creature may experience fear and/or desire when crossing a highway and is capable of evaluating crossing strategies for adoption or rejection. Our simulations show that the creatures’ population success of crossing a single lane unidirectional traffic highway is influenced by the conditions of the environment; i.e. the cars density, the presence or absence of the “erratic drivers”, and by the levels of the fear and/or desire each creature may experience. Creatures’ ability to move along the highway and preservation of the knowledge base from one learning environment to another one improves creatures’ population success of crossing the highway.

The influence of random interactions and decision heuristics on norm evolution in social networks

In this paper we explore the effect that random social interactions have on the emergence and evolution of social norms in a simulated population of agents. In our model agents observe the behaviour of others and update their norms based on these observations. An agent's norm is influenced by both their own fixed social network plus a second random network that is composed of a subset of the remaining population. Random interactions are based on a weighted selection algorithm that uses an individual's path distance on the network to determine their chance of meeting a stranger. This means that friends-of-friends are more likely to randomly interact with one another than agents with a higher degree of separation. We then contrast the cases where agents make highest utility based rational decisions about which norm to adopt versus using a Markov Decision process that associates a weight with the best choice. Finally we examine the effect that these random interactions have on the evolution of a more complex social norm as it propagates throughout the population. We discover that increasing the frequency and weighting of random interactions results in higher levels of norm convergence and in a quicker time when agents have the choice between two competing alternatives. This can be attributed to more information passing through the population thereby allowing for quicker convergence. When the norm is allowed to evolve we observe both global consensus formation and group splintering depending on the cognitive agent model used.

OMAS—a flexible multi-agent environment for CSCWD

CSCW requires efficient support to distributed teams. Several technologies seem to be good candidates for supporting CSCW among which groupware or agents. Groupware is efficient but rather inflexible, multi-agent systems are more flexible but difficult to implement. OMAS (Open Multi-Agent System) is a multi-agent platform derived from a number of previous research projects and teaching courses. With OMAS we tried to answer the problems of architecture, skill definition, protocol implementation, ontologies, dialog using natural language, and debugging, so as to simplify the work of an application designer. OMAS uses a “plug in” type approach offering a model of generic cognitive agent. Agents are of three types: service, personal assistants and transfer. They are organized as coteries, meaning that each agent sees all messages. Agents have separate ontologies and tasks models. Agents are multi-threaded and can execute several tasks concurrently. Personal assistants have prototype dialogs and vocal interface. OMAS has been designed for supporting CSCWD projects or applications. It can be downloaded from the Internet.

Motivation for a new formal framework for agent-oriented software engineering

Agent-Oriented Software Engineering (AOSE) poses several challenges to the traditional theory and practice of software engineering with the emergence of a variety of theories and tools proposed to deal with this challenge. Agent frameworks with formal foundations have an important role to play in this scenario by allowing analysis based on a mathematical model. It is our contention that the available frameworks and languages do not fulfil all aspects of agent-based system analysis and development. We perform a survey of the available formal theories and tools for Multi-Agent Systems (MAS) and derive a strong motivation for a new formal framework to support AOSE. Several aspects of agent behaviour in this framework have been inspired by the popular Belief-Desire-Intention (BDI) model of cognitive agents. The new framework introduced concisely in the second half tackles the issues raised in the first half, including those regarding mobility, proactive behaviour, organisation and inter-agent communication.

A cognitively based simulation of academic science

The models used in social simulation to date have mostly been very simplistic cognitively, with little attention paid to the details of individual cognition. This work proposes a more cognitively realistic approach to social simulation. It begins with a model created by Gilbert (1997) for capturing the growth of academic science. Gilbert's model, which was equation-based, is replaced here by an agent-based model, with the cognitive architecture CLARION providing greater cognitive realism. Using this cognitive agent model, results comparable to previous simulations and to human data are obtained. It is found that while different cognitive settings may affect the aggregate number of scientific articles produced, they do not generally lead to different distributions of number of articles per author. The paper concludes with a discussion of the correspondence between the model and the constructivist view of academic science. It is argued that using more cognitively realistic models in simulations may lead to novel insights.

A Hybrid Agent-Control System Approach to Analyze Various Driving Behaviors

A large percentage of traffic problems occur due to risky or aggressive behavior by drivers (James, 2000). Aggressive driving, incidents of road rage, and following distance are some of the major concerns surrounding the issue, and are increasing in frequency and severity on America's roadways (Rathbone, 1999). An understanding of risky driving and its impact is required in order to help alleviate factors that contribute to these behaviors. Models of risky or aggressive behaviors of drivers allow us to study these effects and their impacts on highway safety, and can further help to identify levers (implicit and explicit) that influence these behaviors. This paper uses a cognitive agent model of human drivers as control systems to aid in the understanding of the effects of heterogeneous beliefs of risk, and the impacts of various driving behaviors on performance.