Rianne van Lambalgen

An Agent Model for a Human's Functional State and Performance

This paper presents an agent model of the dynamics of a humanpsilas functional state in relation to task performance and environment. It can be used in agent systems that support humans in demanding circumstances. Simulation experiments under different parameter settings pointed out that the model is able to produce realistic behaviour of different types of personalities. Moreover, by a mathematical analysis the equilibria of the model have been determined, and by automated checking a number of expected properties of the model have been confirmed.

Modeling situation awareness in human-like agents using mental models

In order for agents to be able to act intelligently in an environment, a first necessary step is to become aware of the current situation in the environment. Forming such awareness is not a trivial matter. Appropriate observations should be selected by the agent, and the observation results should be interpreted and combined into one coherent picture. Humans use dedicated mental models which represent the relationships between various observations and the formation of beliefs about the environment, which then again direct the further observations to be performed. In this paper, a generic agent model for situation awareness is proposed that is able to take a mental model as input, and utilize this model to create a picture of the current situation. In order to show the suitability of the approach, it has been applied within the domain of F-16 fighter pilot training for which a dedicated mental model has been specified, and simulations experiments have been conducted.

Attention Manipulation for Naval Tactical Picture Compilation

This paper discusses and evaluates an agent model that is able to manipulate the visual attention of a human, in order to support naval crew. The agent model consists of four submodels, including a model to reason about a subject’s attention. The model was evaluated based on a practical case study which was formally analysed and verified using automated checking tools. Results show how a human subject’s attention is manipulated by adjusting luminance, based on assessment of the subject’s attention. These first evaluations of the agent show a positive effect.

Automated Visual Attention Manipulation

In this paper a system for visual attention manipulation is introduced and formally described. This system is part of the design of a software agent that supports naval crew in her task to compile a tactical picture of the situation in the field. A case study is described in which the system is used to manipulate a human subjects attention. To this end the system includes a Theory of Mind about human attention and uses this to estimate the subjects current attention, and to determine how features of displayed objects have to be adjusted to make the attention shift in a desired direction. Manipulation of attention is done by adjusting illumination according to the calculated difference between a model describing the subjects attention and a model prescribing it.

An integrated agent model addressing situation awareness and functional state in decision making

In this paper, an integrated agent model is introduced addressing mutually interacting Situation Awareness and Functional State dynamics in decision making. This shows how a humans functional state, more specific a humans exhaustion and power, can influence a humans situation awareness, and in turn the decision making. The model is illustrated by a number of simulation scenarios.

A Generic Personal Assistant Agent Model for Support in Demanding Tasks

Human task performance may vary depending on the characteristics of the human, the task and the environment over time. To ensure high effectiveness and efficiency of the execution of tasks, automated personal assistance may be provided to task performers. A personal assistant agent may constantly monitor the humans state and task execution, analyse the state of the human and task, and intervene when a problem is detected. This paper proposes a generic design for a Personal Assistant agent model which can be deployed in a variety of domains. Application of the Personal Assistant model is illustrated by a case study from the naval domain.

A system to support attention allocation: Development and application

This paper discusses and evaluates an agent model that is able to manipulate the visual attention of a human, in order to support naval crew. The agent model consists of four sub-models, including a model to reason about a subjects attention. The model was evaluated based on a practical case study which was formally analyzed and verified using automated checking tools. Results show how a human subjects attention is manipulated by adjusting luminance, based on assessment of the subjects attention. These first evaluations of the agent show a positive effect.

Adaptation and Validation of an Agent Model of Functional State and Performance for Individuals

Human performance can seriously degrade under demanding tasks. To improve performance, agents can reason about the current state of the human, and give the most appropriate and effective support. To enable this, the agent needs a model of a specific persons functional state and performance, which should be valid, as the agent might otherwise give inappropriate advice and even worsen performance. This paper concerns the adaptation of the parameters of the existing functional state model to the individual and validation of the resulting model. First, human experiments have been conducted, whereby measurements related to the model have been performed. Next, this data has been used to obtain appropriate parameter settings for the model, describing the specific subject. Finally, the model, with the tailored parameter settings, has been used to predict human behavior to investigate predictive capabilities of the model. The results have been analyzed using formal verification.

Learning belief connections in a model for situation awareness

Situational awareness is critical in many human tasks, especially in cases where humans have to make decisions fast and where the result of their decisions might affect their life. This paper addresses the problem of learning optimal values for the parameters of a situational awareness model. The model is a complex network with nodes connected by links with weights, which connect observations to simple beliefs, such as “there is a contact”, to complex belief, such as “the contact is hostile”, and to future beliefs, such as “it is possible the pilot is being targeted”. The model has been built and validated by human experts in the domain of F16 fighter pilots and is used to study human decision making. Given the complexity of the model, there is a need to learn appropriate weights for the connections, which, in turn, affect the activation levels of the beliefs. We propose the use of a genetic algorithm and of a sensitivity based approach to learn the weights in the model. Extensive experimental results are included.

An Agent Model for Analysis of Human Performance Quality

A human’s performance in a complex task is highly dependent on the demands of the task, in the sense that highly demanding situations will often cause a degradation of performance. To maintain performance quality usually extra effort has to be contributed. However, the resources for such extra effort available to the human are limited. In this paper an agent model is proposed in which different types of relations between effort, task demands and performance quality can be used to analyse the human’s performance quality. It is illustrated how a support agent incorporating this model can support a human based on different performance criteria. The agent model thus allows to build agent applications that provide optimal support depending on a specific situation and goal of the task.