Arabic Subject Markers are interface phenomena (specifically between morphology and syntax). In this paper, I describe them briefly, I give my linguistic analysis within the framework of the Head-Driven Phrase Structure Grammar and I show how I implement them in the LKB system. I show that this system, despite its strength, does not allow for a proper implementation of these units.

Quality management, analysis and evaluation of intelligent systems are important tasks. This paper proposes a data mining approach based on the technique of subgroup mining utilizing a data warehouse that contains data from the respective intelligent system to be evaluated and from other external sources. The context of our work is given by an intelligent documentation and consultation system in the medical domain of sonography. For demonstrating the applicability and benefit of the presented approach, we provide several realworld examples of a case-study applying the approach in the medical domain of sonography.

This paper addresses the problem of how a group of agents cooperating on a complex plan with interdependent actions can coordinate their scheduling and execution of those actions, particularly in domains where actions may fail or have uncertain durations.  If actions fail (or fail to meet their deadlines), the repercussions for the rest of the team's plan can be dramatic.  This paper presents a pro-active strategy, called Just-in-Time Backfilling (JIT-BF), that agents can use to increase the fault tolerance of their interdependent schedules by identifying actions in danger of failing and inserting redundant (or back-up) actions into their schedules.  The insertion of redundant actions can be done locally (i.e., by the agent whose action is in danger of failing) or through negotiations with the rest of the team. The computations performed by agents following the JIT-BF strategy depend on probabilistic models of action durations and the ``quality'' achieved by successfully executing actions.  The paper presents an experimental evaluation of the JIT-BF strategy within a simulated real-time dynamic environment that demonstrates that teams using the pro-active JIT-BF strategy significantly out-perform teams that rely solely on reactive strategies.

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.

We studied the effect of post-practice reflection on learning, using programming tutors, and multiple-choice format for reflection. We conducted in-vivo controlled studies with introductory programming students from multiple schools over 3 semesters, and used mixed-factor ANOVA to analyze the collected data. We found that reflecting on the concept underlying each problem neither promotes greater learning, measured as pre-post increase in the average score per problem, nor promotes faster learning, measured as the problems solved per concept learned. We conjecture that the benefits of reflecting on the concept underlying each problem may be limited if a tutor already promotes deep understanding of the domain.

Analogical reasoning is considered as one of the main mechanisms underlying creativity. "Thinking out of the box" allows the paradigm shift essential to a creative process. More common is the concept of analogical proportion ("2 is to 4 as 4 is to 8") which can be described within an algebraic framework. When it comes to concepts ("engine is to the car as heart is to the human"), we need to investigate a new way to understand this analogical ratio. In this paper, we take inspiration from the formal framework of information theory for proposing a new approach to the evaluation of analogy between concepts. Using Kolmogorov complexity as a backbone providing a clear semantics, we give a practical interpretation for analogy between words viewed as labeling concepts. Making use of Google as a linguistic resource, we provide an implementation of our definitions: experiments show that the accuracy of our definition is quite acceptable and justify the approach.

Highly capable multiple robot architectures often resort to micromanagement to provide enhanced cooperative abilities, sacrificing individual autonomy. Conversely, multi-robot architectures that maintain individual autonomy are often limited in their cooperative abilities.  This article presents a modified three layer architecture that solves both of these issues.  The addition of a Coordinator layer to a three-layered approach provides a platform-independent interface for coordination on tasks and takes advantage of individual autonomy to improve coordination capabilities.  This reduces communication overhead versus many multi-robot architecture designs and allows for more straightforward resizing of the robot collective and increased individual autonomy.

In this paper we present a syntactical way to develop the adaptation capability in logical-based intelligent agents. We use prime implicants to represent the beliefs of an agent and present how syntactical belief update operators can be obtained by correlating models and prime implicants. Using prime implicants allows the introdution a new notion of belief update. We characterize this new operator both in terms of postulates and in terms of explicit operators.

This paper describes the Spyglass tool, which is designed to help analysts explore very large collections of unstructured text documents. Spyglass uses a domain ontology to index documents, and provides retrieval and visualization services based on the ontology and the resulting index. The ontology based approach allows analysts to share information and helps to ensure consistency of results. The approach is also scalable and lends itself very well to parallel computation. The Spyglass system is described in detail and indexing and query results using a large set of sample documents are presented.

This paper extends the concept of trust modeling within a multi-agent environment.  Trust modeling often focuses on identifying the appropriate trust level for the other agents in the environment and then using these levels to determine how to interact with each agent.  However, this type of modeling does not account for sneaky agents who are willing to cooperate when the stakes are low and take selfish, greedy actions when the rewards rise.  Adding trust to an interactive partially observable Markov decision process (I-POMDP) allows trust levels to be continuously monitored and corrected enabling agents to make better decisions.  The addition of trust modeling increases the decision process calculations, but solves more complex trust problems that are representative of the human world.  The modified I-POMDP reward function and belief models can be used to accurately track the trust levels of agents with hidden agendas.  Testing demonstrates that agents quickly identify the hidden trust levels to mitigate the impact of a deceitful agent.