Towards Supporting Psychologically Plausible Variability in Agent-Based Human Modelling

We describe the initial steps in developing an agentbased cognitive architecture designed to support psychologically plausible human variability. The new architecture, COJACK, is based on JACK, a BDI-based agent language. It will constrain the agents to reason and act in a psychologically plausible manner. Their information processing will be adjusted by a set of parameters that moderate the agent's reasoning and actions, combined with a set of guidelines for developing plans and beliefs for the agents. This set of parameters will also support varying the agents' performance, both in terms of differences across agents as well as differences that arise within an individual due to internal and external factors. We conclude that other architectures will want to include a similar set, including representing a body, its interaction with the environment, and the passage of time

Критика Юркевичем утилітаризму

This work focuses on studying players behaviour in interactive narratives with the aim to simulate their choices. Besides sub-optimal player behaviour due to limited knowledge about the environment, the difference in each player's style and preferences represents a challenge when trying to make an intelligent system mimic their actions. Based on observations from players interactions with an extract from the interactive fiction Anchorhead, we created a player profile to guide the behaviour of a generic player model based on the BDI (Belief-Desire-Intention) model of agency. We evaluated our approach using qualitative and quantitative methods and found that the player profile can improve the performance of the BDI player model. However, we found that players self-assessment did not yield accurate data to populate their player profile under our current approach.Comment: CHI Play 201

Kommuners användande av Facebook och Twitter i kriskommunikation

The purpose of this study is to create an understanding for crisis communication in four different municipalities. The main question is how these municipalities have been working with crisis communication on the social media, Facebook and Twitter, during 2012-2017. The study is of the qualitative type and the method is based on interviews with communication managers and senior communication managers. In combination with interviews, a rhetorical analyze has been done of the municipalities’ crisis announcement, on Facebook and Twitter. The earlier research and theory which this study is based on, is related areas in crisis communication. Focus is also set on definitions of crisis and the results shows that Facebook has been the central part of the crisis communications to these specific municipalities compared to Twitter. In the analysis it also shows that none of the municipalities has been involved in big crisis during this period

Learning to Notice: Adaptive Models of Human Operators

Agent-based technologies have been used for a number of years to model human operators in complex simulated environments. The BDI agent framework has proven to be particularly suited to this sort of modelling, due to its "natural" composition of beliefs, goals and plans. However one of the weaknesses of the BDI agent model, and many other human operator models (agent-based or otherwise), is its inability to support agent learning. Human operators naturally adapt their behaviour over time, particularly to avoid repeating mistakes. This paper introduces an enhancement to the BDI framework which is based on a descriptive psychological model of decision making called "recognition-primed decision making." This enhancement allows the development of agents that adapt their behaviour in real-time, in the same manner as a person would, providing more realistic human operator models

Modelling human behaviour with BDI agents

Deposited with permission of the author. © 2009 Dr. Emma Jane NorlingAlthough the BDI framework was not designed for human modelling applications, it has been used with considerable success in this area. The work presented here examines some of these applications to identify the strengths and weaknesses of the use of BDI-based frameworks for this purpose, and demonstrates how these weaknesses can be addressed while preserving the strengths. The key strength that is identified is the framework's folk-psychological roots, which facilitate the knowledge acquisition and representation process when building models. Unsurprisingly, because the framework was not designed for this purpose, several shortcomings are also identified. These fall into three different classes. Firstly, although the folk-psychological roots mean that the framework captures a human-like reasoning process, it is at a very abstract level. There are many generic aspects of human behaviour - things that are common to all people across all tasks - which are not captured in the framework. If a modeller wishes to take these things into account in a model, they must explicitly encode them, replicating this effort for every model. To reduce modellers' workload and increase consistency, it is desirable to incorporate such features into the framework. Secondly, although the folk-psychological roots facilitate knowledge acquisition, there is no standardised approach to this process, and without experience it can be very difficult to gather the appropriate knowledge from the subjects to design and build models. And finally, these models must interface with external environments in which they 'exist.' There are often mismatches in the data representation level which hinder this process. This work makes contributions to dealing with each of these problems, drawing largely on the folk-psychological roots that underpin the framework. The major contribution is to present a systematic approach to extending the BDI framework to incorporate further generic aspects of human behaviour and to demonstrate this approach with two different extensions. A further contribution is to present a knowledge acquisition methodology which gives modellers a structured approach to this process. The problems at the agent-environment interface are not straightforward to solve, because sometimes the problem lies in the way that the environment accepts and receives data. Rather than offering the golden solution to this problem, the contribution provided here is to highlight the different types of mismatches that may occur, so that modellers may recognise them early and adapt their approach to accommodate them