Dilhan J. Thilakarathne

Computational cognitive modelling of action awareness: prior and retrospective

This paper presents a computational cognitive model for action awareness focusing on action preparation and performance by considering its cognitive effects and affects from both prior and retrospective form relative to the action execution. How action selection and execution contribute to the awareness or vice versa is a research question, and from the findings of brain imaging and recording techniques more information has become available on this. Some evidence leads to a hypothesis that awareness of action selection is not directly causing the action execution (or behaviour) but comes afterwards as an effect of unconscious processes of action preparation. In contrast, another hypothesis claims that both predictive and inferential processes related to the action preparation and execution may contribute to the conscious awareness of the action, and furthermore, this awareness of an action is a dynamic combination of both prior awareness (through predictive motor control processes) and retrospective awareness (through inferential sense-making processes) relative to the action execution. The proposed model integrates the findings of both conscious and unconscious explanations for both action awareness and ownership and acts as a generic computational cognitive model to explain agent behaviour through the interplay between conscious and unconscious processes. Validation of the proposed model is achieved through simulations on suitable scenarios which are covered with actions that are prepared without being conscious at any point in time, and also with the actions that agent develops prior awareness and/or retrospective awareness. Having selected an interrelated set of scenarios, a systematic approach is used to find a suitable but generic parameter value set which is used throughout all the simulations that highlights the strength of the design of this cognitive model.

Modelling Prior and Retrospective Awareness of Actions

Agents often may prepare for and perform actions without being conscious of these processes. However, in other cases, at some point in time the agent can develop some awareness state relating to the action. This can be an awareness state prior to the execution of the action. An awareness state can also develop in retrospect, after the action was performed. In this paper a neurologically inspired agent model is introduced that is able to make such distinctions. Scenarios are covered in which actions are prepared without being conscious at any point in time. Also scenarios are covered in which the agent develops proir awareness or retrospective awareness, or both. When prior awareness is developed it may be the case that this awareness has a decisive effect on actually executing the action, but it may equally well be the case that the awareness state has no effect on whether the action is performed. All these variations have been illustrated by a wide variety of simulation experiments.

Modelling the dynamics of emotional awareness

In this paper, based on literature from Cognitive and Affective Neuroscience, a computational agent model is introduced incorporating the role of emotional awareness states in the dynamics of action generation. More specifically, it covers both automatic, unconscious (bottom-up) and more cognitive and conscious (top-down) emotion generation processes, and their mutual interaction. The model was formalised in a dynamical system format. In different scenarios the model shows simulation results that are in line with patterns reported in literature.

Modelling Dynamics of Cognitive Control in Action Formation with Intention, Attention, and Awareness

Human action formation primarily concerns automatic brain processes that are responsive to a salient stimulus. Nevertheless, the importance of studying the control of these actions to obtain more flexible and self-regulated behaviours under the intervention of top-down related processes has been noted. In this paper a top-down guided action formation based on automatic pathways with the cognitive states intention, attention, and awareness has been modelled. By simulations the validity of the model has been explored. This model will be used in scrutinizing the interplay among conscious and unconscious processes in clinical disorders, as a workbench for cognitive scientists, and in agent-based applications for healthy lifestyle, and complex systems that involve human cognition.

Agent-Based Analysis of Annual Energy Usages for Domestic Heating based on a Heat Pump

This paper describes an agent-based analysis approach to determine in which way a net zero house can be obtained. In particular, it addresses agent-based simulation to estimate annual energy usage for heating based on an air to water heat pump. Based on the introduced approach house owners will be able to decide on the specifications for further renewable energy production systems to be installed, for example, solar or wind energy production systems in order to obtain a net zero house in the present and in future years.

Neurologically Inspired Computational Cognitive Modelling of Situation Awareness

How information processes in the human brain relate to action formation is an interesting research question and with the latest development of brain imaging and recording techniques more and more interesting insights have been uncovered. In this paper a cognitive model is scrutinized which is based on cognitive, affective, and behavioural science evidences for situation awareness. Situation awareness has been recognized as an important phenomenon in almost all domains where safety is of highest importance and complex decision making is inevitable. This paper discusses analysis, modelling and simulation of three scenarios in the aviation domain where poor situation awareness plays a main role, and which have been explained by Endsley according to her three level situation awareness model. The computational model presented in this paper is driven by the interplay between bottom-up and top-down processes in action formation together with processes and states such as: perception, attention, intention, desires, feeling, action preparation, ownership, and communication. This type of cognitively and neurologically inspired computational models provide new directions for the artificial intelligence community to develop systems that are more aligning with realistic human mental processes and for designers of interfaces of complex systems.

Modelling the Role of Cognitive Metaphors in Joint Decision Making

In this paper, a social agent model is presented for the influence of cognitive metaphors on joint decision making processes. The social agent model is based on mechanisms known from cognitive and social neuroscience and cognitive metaphor theory. The model was illustrated in particular for two types of metaphors that can affect joint decision making in different manners: a cooperative metaphor and a competitive metaphor. By a number of scenarios it was shown how the obtained social agent model can be used to simulate and analyze joint decision processes influenced by cognitive metaphor.

A Neurologically Inspired Model of the Dynamics of Situation Awareness Under Biased Perception

This paper presents a computational cognitive agent model of Situation Awareness SA, 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, awareness, ownership, feeling, and communication. The emphasis is on explaining the cognitive basis for biased perception in SA, which is considered to be the most frequent factor in poor SA the reason for 76% of poor SA errors, through perceptual load. A model like this will be useful in applications which relay on complex simulations e.g. aviation domain that need computational agents to represent human action selection together with cognitive details. 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.

A Parameter Estimation Method for Dynamic Computational Cognitive Models

Abstract A dynamic computational cognitive model can be used to explore a selected complex cognitive phenomenon by providing some features or patterns over time. More specifically, it can be used to simulate, analyse and explain the behaviour of such a cognitive phenomenon. It generates output data in the form of time series which can only be partially compared to empirical knowledge. This leads to a challenging problem to estimate values of the parameters of the model representing characteristics of a person. A parameter estimation approach for dynamic cognitive models is presented here by combining improved Particle Swarm Optimization (PSO) and Constraint Satisfaction (CS) methods. Having collected the key features of behaviour of a phenomenon, those are translated into a set of constraints with parameters that will be solved through an improved agent based PSO technique. Through this, within PSO each agent explores the complex search space while communicating the quality of a local parameter value vector relative to their current global best solution as a swarm (through cooperation and competition). This is performed in tournaments and results of each tournament are combined to address the premature convergence issue in PSO.

INTELLIGENT AND PERSONALIZED TRAVEL PLANNING SYSTEM FOR TOURISM THROUGH A COGNITIVE INSPIRED FRAMEWORK

The tourism industry has become a vital economical factor for certain countries which have been blessed by the beauty of Mother Nature, including Sri Lanka. Nevertheless; to ensure sustainable development of tourism together with the infrastructure development, one main factor to be improved is the dissemination of information on dynamic tour planning. This paper mainly focuses on the hypothesis that dynamic travel planning can be managed through a cognitive inspired framework. Furthermore; this model will consider the dynamic mental interests of the user to facilitate a comprehensive tour planning together with consideration for the services of accommodation, transportation, recreation, etc. This proposed system developed on top of a cognitive model with agent technology. The realized cognitive model contains a Short Term Memory together with three Episodic, Semantic, and Procedural entities as Long Term Memory. To promptly enable the communication among heterogeneous entities a unified structure for ontology has been realized and relevant entities can use that when updating their information or extending the functionality. A prototyped tour planning application has been developed to scrutinize the effectiveness of a cognitive framework. It shows that this approach is adaptable in dynamic environments together with personalized user interests and novelty in the solution.