The gaming industry has reached a point where improving graphics has only a small effect on how much a player will enjoy a game. The focus has turned to adding more humanlike characteristics into computer game agents. Machine learning techniques are scarcely being used in games, although they do offer powerful means for creating humanlike behaviors in agents. The first person shooter (FPS), Quake 2, is an open source game that offers a multi-agent environment in which to create game agents (bots). The work described in this paper seeks to combine neural networks with a modeling paradigm known as context based reasoning (CxBR) to create a contextual game observation (CONGO) system that produces humanlike Quake 2 bots. A default level of intelligence is instilled into the bots through contextual scripts to prevent the bot from being trained to be completely useless. The results show that the humanness and entertainment value as compared to a traditional scripted bot have improved, although, CONGO bots usually ranked only slightly above a novice skill level. Overall, CONGO offers the gaming community a mode of game play that has promising entertainment value.

With the most resource intensive tasks in games offloaded to special purpose processors, game designers now have the opportunity to build richer characters using more complex AI techniques than have been used in the past. While additional CPU time makes improved AI feasible, better tools for building agents are needed to make good interactive characters a reality. In this paper we present the BEHAVEngine and BehaviorShop which enable the creation of rich interactive characters.

We present a modified navigation mesh generation algorithm that allows the mesh to be dynamically altered at runtime. We accomplish this using an extension to the existing spatial decomposition algorithm ASFV (Adaptive Space Filling Volumes) that will allow the algorithm to dynamically adapt to changes to the underlying world geometry without having to rebuild the entire spatial decomposition. This is accomplished by providing two algorithms to deal with alterations to the world. The ability is provided to add arbitrary obstructions into what was negative space and then to build a new correct spatial decomposition around the new obstruction. Functionality is also provided to remove existing obstructions and then to build up new decompositions to fill in the newly created negative space. Finally, we show via an experiment that our dynamic extensions to ASFV reduces the cost of correcting an invalidated decomposition by 90% or more.

Generally, learning systems suffer from a lack of an explicit and adaptable didactic design. A previously introduced modeling approach called storyboarding is setting the stage to apply Knowledge Engineering Technologies to verify and validate the didactics behind a learning process. Moreover, didactics can be refined according to revealed weaknesses and proven excellence. Successful didactic patterns can be explored by applying mining techniques to the various ways students went through the storyboard and their associated level of success.

What is the biggest difference between playing a game against a human or against a computer generated player? Why do many people believe it is more challenging to play with humans than playing with an artificial player? The big success of massive multiplayer games and the huge number of so-called "LAN parties", where players meet and play with each other, seems to be related to the human demeanor of the players. All this indicates, that the current state of game AI is unsatisfactory compared to the performance of human players. This paper introduces a tool for analyzing basic computer games with incorporated AI modules which store strategies for performing the behavior of artificial players. This sets the stage for a systematic evaluation and refinement of rule based game AI.

Manual knowledge acquisition is usually a costly and time-consuming process. Automatic knowledge acquisition methods can then significantly support the knowledge engineer. In this paper, we propose an approach for rapid knowledge capture. The methodology is based on textual subgroup mining in order to discover dependencies for rule prototyping.

Ensuring the consistency and completeness of Semantic Web ontologies is practically impossible, because of their scale and highly dynamic nature. Many web applications, therefore, must deal with vague, incomplete and even inconsistent knowledge. Rules were shown to be very effective in processing such knowledge, and future web services are expected to depend heavily on them. RuleML, which is the earliest effort to define a normalized markup for representing and exchanging rules on the web, is currently limited to Horn rules. Significant research efforts are underway to extend RuleML with more flexible representation and reasoning capabilities. This paper presents an extension of the current rule format intended to accommodate uncertain and/or inconsistent knowledge, and shows how one truth maintenance logic can be adapted and extended to support such rules.

Commonsense reasoning, in particular qualitative spatial and temporal reasoning (QSTR), provides flexible and intuitive methods for reasoning about vague and uncertain information including spatial orientation, topology and proximity.  Despite a number of theoretical advances in QSTR, there are relatively few applications that employ these methods.  The central problem is a significant lack of application level standards and validation methods for supporting developers in adapting and integrating QSTR with their domain specific qualitative spatial and temporal models.  To address this we present a significantly novel methodology for QSTR application validation, inspired by research in software engineering.  In this paper we focus on unit testing, and adapt the software engineering strategy of defining boundary cases.  We present two critical boundary concepts, a methodology for isolating the units under testing from other parts of the model, and methods to assist the designer in integrating our critical boundary unit testing approach with a broader validation plan.

In this paper knowledge representation and inference issues for rule-based systems are discussed. The paper deals with improving the logical calculus of Set Attributive Logic founding an expressive rule language XTT2. Representation extensions are introduced, and practical inference rules provided. The original includes an extended state specification, as well as interpreter design. xamples of rule analysis are given. Visual design tool HQed assuring rule quality is also presented.

In this paper certain knowledge and software engineering methods integration issues are discussed. The principal idea is to consider an effective design and implementation framework for rule design with UML, and implementation with Java. The solution proposed in the paper consists of using a custom knowledge engineering design method for rules in the design stage. The rule base is then transformed to UML behavioral diagrams, which can be considered a visual encoding. The rule implementation involves the serialization to Java language using classes representing the decision tables grouping rules sharing the same attributes.