Realistic Dialogue Engine for Video Games

The concept of believable agent has a long history in Artificial Intelligence. It has applicability in multiple fields, particularly video games. Video games have shown tremendous technological advancement in several areas such as graphics and music; however, techniques used to simulate dialogue are still quite outdated. In this thesis, a method is proposed to allow a human player to interact with non-player characters using natural-language input. By using various techniques of modern Artificial Intelligence such as information retrieval and sentiment analysis, non-player characters have the capability of engaging in dynamic dialogue: they can answer questions, ask questions, remember events, and more. This conversation system is highly customizable, so the types of responses that non-player characters give can be modified to fit within a game’s storyline. Although the system only currently allows for simple dialogue, it illustrates the potential for a more robust way to simulate believable agents in video games

Final report on the farmer's aid in plant disease diagnoses

This report is the final report on the FAD project. The FAD project was initiated in september 1985 to test the expert system shell Babylon by developing a prototype crop disease diagnosis system in it. A short overview of the history of the project and the main problems encountered is given in chapter 1. Chapter 2 describes the result of an attempt to integrate JSD with modelling techniques like generalisation and aggregation and chapter 3 concentrates on the method we used to elicit phytopathological knowledge from specialists. Chapter 4 gives the result of knowledge acquisition for the 10 wheat diseases most commonly occurring in the Netherlands. The user interface is described briefly in chapter 5 and chapter 6 gives an overview of the additions to the implementation we made to the version of FAD reported in our second report. Chapter 7, finally, summarises the conclusions of the project and gives recommendations for follow-up projects

Moving Usability Testing onto the Web

Abstract: In order to remotely obtain detailed usability data by tracking user behaviors within a given web site, a server-based usability testing environment has been created. Web pages are annotated in such a way that arbitrary user actions (such as "mouse over link" or "click back button") can be selected for logging. In addition, the system allows the experiment designer to interleave interactive questions into the usability evaluation, which for instance could be triggered by a particular sequence of actions. The system works in conjunction with clustering and visualization algorithms that can be applied to the resulting log file data. A first version of the system has been used successfully to carry out a web usability evaluation

Adaptive Decision Support for Academic Course Scheduling Using Intelligent Software Agents

Academic course scheduling is a complex operation that requires the interaction between different users including instructors and course schedulers to satisfy conflicting constraints in an optimal manner. Traditionally, this problem has been addressed as a constraint satisfaction problem where the constraints are stationary over time. In this paper, we address academic course scheduling as a dynamic decision support problem using an agent-enabled adaptive decision support system. In this paper, we describe the Intelligent Agent Enabled Decision Support (IAEDS) system, which employs software agents to assist humans in making strategic decisions under dynamic and uncertain conditions. The IAEDS system has a layered architecture including different components such as a learning engine that uses historic data to improve decision-making and an intelligent applet base that provides graphical interface templates to users for frequently requested decision-making tasks. We illustrate an application of our IAEDS system where agents are used to make complex scheduling decisions in a dynamically changing environment

A workshop on the gathering of information for problem formulation

Issued as Quarterly progress reports no. [1-5], Proceedings and Final contract report, Project no. G-36-651Papers presented at the Workshop/Symposium on Human Computer Interaction, March 26 and 27, 1981, Atlanta, G

Influences on aircraft target off-block time prediction accuracy

With Airport Collaborative Decision Making (A-CDM) as a generic concept of working together of all airport partners, the main aim of this research project was to increase the understanding of the Influences on the Target Off-Block Time (TOBT) Prediction Accuracy during A-CDM. Predicting the TOBT accurately is important, because all airport partners use it as a reference time for the departure of the flights after the aircraft turn-round. Understanding such influencing factors is therefore not only required for finding measures to counteract inaccurate TOBT predictions, but also for establishing a more efficient A-CDM turn-round process. The research method chosen comprises a number of steps. Firstly, within the framework of a Cognitive Work Analysis, the sub-processes as well as the information requirements during turn-round were analysed. Secondly, a survey approach aimed at finding and describing situations during turn-round that are critical for TOBT adherence was pursued. The problems identified here were then investigated in field observations at different airlines’ operation control rooms. Based on the findings from these previous steps, small-scale human-in-the-loop experiments were designed aimed at testing hypotheses about data/information availability that influence TOBT predictability. A turn-round monitoring tool was developed for the experiments. As a result of this project, the critical chain of turn-round events and the decisions necessary during all stages of the turn-round were identified. It was concluded that information required but not shared among participants can result in TOBT inaccuracy swings. In addition, TOBT predictability was shown to depend on the location of the TOBT turn-round controller who assigns the TOBT: More reliable TOBT predictions were observed when the turn-round controller was physically present at the aircraft. During the experiments, TOBT prediction could be improved by eight minutes, if available information was cooperatively shared ten minutes prior turn-round start between air crews and turn-round controller; TOBT prediction could be improved by 15 minutes, if additional information was provided by ramp agents five minutes after turnround start