Software agents & human behavior

People make important decisions in emergencies. Often these decisions involve high stakes in terms of lives and property. Bhopal disaster (1984), Piper Alpha disaster (1988), Montara blowout (2009), and explosion on Deepwater Horizon (2010) are a few examples among many industrial incidents. In these incidents, those who were in-charge took critical decisions under various ental stressors such as time, fatigue, and panic. This thesis presents an application of naturalistic decision-making (NDM), which is a recent decision-making theory inspired by experts making decisions in real emergencies. This study develops an intelligent agent model that can be programed to make human-like decisions in emergencies. The agent model has three major components: (1) A spatial learning module, which the agent uses to learn escape routes that are designated routes in a facility for emergency evacuation, (2) a situation recognition module, which is used to recognize or distinguish among evolving emergency situations, and (3) a decision-support module, which exploits modules in (1) and (2), and implements an NDM based decision-logic for producing human-like decisions in emergencies. The spatial learning module comprises a generalized stochastic Petri net-based model of spatial learning. The model classifies routes into five classes based on landmarks, which are objects with salient spatial features. These classes deal with the question of how difficult a landmark turns out to be when an agent observes it the first time during a route traversal. An extension to the spatial learning model is also proposed where the question of how successive route traversals may impact retention of a route in the agent’s memory is investigated. The situation awareness module uses Markov logic network (MLN) to define different offshore emergency situations using First-order Logic (FOL) rules. The purpose of this module is to give the agent the necessary experience of dealing with emergencies. The potential of this module lies in the fact that different training samples can be used to produce agents having different experience or capability to deal with an emergency situation. To demonstrate this fact, two agents were developed and trained using two different sets of empirical observations. The two are found to be different in recognizing the prepare-to-abandon-platform alarm (PAPA ), and similar to each other in recognition of an emergency using other cues. Finally, the decision-support module is proposed as a union of spatial-learning module, situation awareness module, and NDM based decision-logic. The NDM-based decision-logic is inspired by Klein’s (1998) recognition primed decision-making (RPDM) model. The agent’s attitudes related to decision-making as per the RPDM are represented in the form of belief, desire, and intention (BDI). The decision-logic involves recognition of situations based on experience (as proposed in situation-recognition module), and recognition of situations based on classification, where ontological classification is used to guide the agent in cases where the agent’s experience about confronting a situation is inadequate. At the planning stage, the decision-logic exploits the agent’s spatial knowledge (as proposed in spatial-learning module) about the layout of the environment to make adjustments in the course of actions relevant to a decision that has already been made as a by-product of situation recognition. The proposed agent model has potential to be used to improve virtual training environment’s fidelity by adding agents that exhibit human-like intelligence in performing tasks related to emergency evacuation. Notwithstanding, the potential to exploit the basis provided here, in the form of an agent representing human fallibility, should not be ignored for fields like human reliability analysis

An enhanced computational integrated decision model for prime decision-making in driving

Recent development of technology has led to the invention of driver assistance systems that support driving and prevent accidents. These systems employ Recognition-Primed Decision (RPD) model that use driver prior experience to make prime decision during emergencies. However, the existing RPD model does not include necessary training factors. Although, there is existing integrated RPD-SA model known as Integrated Decision-making Model (IDM) that includes training factors from Situation Awareness (SA) model, the training factors were not detailed (IDM has only six training factors). Hence, the model could not provide reasoning capability. Therefore, this study enhanced the IDM by proposing Computational-Rabi’s Driver Training (C-RDT) model that improves the RPD component with 18 additional training factors obtained from cognitive theories. The designed model is realized by identifying factors for prime decision-making in driving domain, designing the conceptual model of the RDT and formalizing it using differential equation. The model is verified through simulation, mathematical and automated analyses and then validated by human experiment. Verification result shows positive equilibrium conditions of the model (stability) and confirms the structural and theoretical correctness of the model. Furthermore, the validation result shows that the inclusion of the 18 training factors in the RPD training component of the IDM can improve driver’s prime decision-making. This study demonstrated the ability of the enhanced C-RDT model to backtrack and provide reasoning on the undertaking decisions. Hence, the model can also serve as a guideline for software developers in developing driving assistance systems

Serious gaming approach framework for construction hazards identification

Construction-related workers are always exposed to occupational hazards ona construction site. Hence, safety training is inevitable to reduce the alarming rate ofaccidents on sites. However, due to the nature of construction environment which ishazardous and harmful current safety training is still lacks hands-on approaches.Training assisted by affordable technology such as serious game would be aneffective tool to improve learning and has become a new approach to trainingdelivery. It offers safer, interactive and entertaining learning environment for theconstruction-related workers. Therefore, the aim of this study is to develop a seriousgame framework for hazard identification training module. To develop thisframework, the Garris‘s Input-Process-Outcome game model is adopted as thefoundation and five objectives are laid out. The first objective is to determine themost suitable instructional design method and the second objective is to determineserious game attributes to support the effective learning. Through content analysismethods, the findings show that there are 12 attributes of the serious game andGagne‘s Nine Events Instructional Methods Design is able to support an effectivelearning. The third objective is to understand user characteristics. Data wascollected from 319 construction-related workers using questionnaires and analysedusing mean comparison and ANOVA. Findings confirmed that they belong toindependent learners‘ category and inclined to ‗vigilant‘ and ‗brooding‘ types ofdecision-making style. These objectives become the basis for Input phases of theframework. The Gagne‘s instructional method also laid out the learning expectationfor Outcome phase i.e. skills, cognitive and affective learning. The fourth objectiveis to design the process of hazard identification. Through content analysis,Recognition-Primed Decision making model (RPD) is chosen and merged withhazard identification process and hierarchy of control to establish the Process phaseof the framework. All the findings are incorporated to achieve the fifth objectivewhich is to develop the serious game framework. The framework is validated bythree experts specialised in education, construction safety, and informationtechnology. They agreed that this framework would be able to enhance learning interm of skills, cognitive and affective learning. Finally, this serious game frameworkwill provide a safer, more affordable and interactive as well as entertaining forhazard identification training delivery in the construction industry