In this paper, we report progress on making synthetic characters more taskable. In particular, we present an English-like command language that lets one specify complex behaviors an agent should carry out in a virtual environment. We also report compilers that translate English commands into a formal notation and formal statements into procedures for Icarus, an agent architecture that supports reactive execution. To demonstrate the benefits of such taskability, we have integrated Icarus with Twig, which provides a simulated physical environment with humanoid agents. We use the command language to specify three complex activities, including responding to an object contingently, collecting and storing a set of objects, and negotiating with another agent in order to purchase an item. We also discuss related work on controlling synthetic characters, along with paths for additional research on taskability.

We use a reactive planning language, ABL (Mateas and Stern 2002), to easily express hierarchical patterns of Creating a good level is a time consuming and iterative geometry that can be incorporated into the level, and also process: designers will typically play a level themselves a monitor and react to designer changes. The geometric number of times before showing it to anyone else, simply relationships between level components are given to a to check that it is playable and meets their expectations constraint solver, Choco (Choco Team 2008), as a set of (Castillo and Novak 2008). Making a change to a small constraints that must be satisfied, thus ensuring that the section of a level, such as moving a single piece of generator will never produce an unplayable level. A geometry, can have a wide impact and require much of the diagram desc rastructure is shown in rest of the level to be modified as well.

Artificial intelligence is frequently used to control virtual characters in movies and games. When these characters appear in crowds, controlling them is called crowd simulation. In this paper, I suggest that crowd simulation could be accomplished by multi-agent reinforcement learning, a method by which groups of agents can learn to act autonomously in their environment. I present a case study that explores the challenges and benefits of this type of approach and encourages the development of learning techniques for AI in entertainment media.

Story Canvas is a visual authoring tool for the creation of interactive, generative stories. Aimed at authors without a technical background in computational storytelling, our system takes an existing author goal-based narrative planning architecture and adds a highly visual authoring and reading interface to the technology, using the language of storyboards and comics as a framework for both authoring and interacting with the resulting narratives. In this paper we describe Story Canvas and its evolution from our previous authoring work, including how our interface choices have been driven by our previous experiences with non-technical authors, and describe the details of translating the visual authoring constructs into story plans within the story generator.

Our goal is to enable Non Player Characters (NPC) in computer games to exhibit natural behaviors. The quality of behaviors affects the game experience especially in story-based games, which rely on player-NPC interactions. We used Reinforcement Learning to enable NPC companions to develop preferences for actions. We implemented our RL technique in BioWare Corp.’s Neverwinter Nights. Our experiments evaluate an NPC companion’s behaviors regarding traps. Our method enables NPCs to rapidly learn reasonable behaviors and adapt to changes in the game.

In the winter, 2004 issue of AI Magazine, we reported Vulcan Inc.'s first step toward creating a question-answering system called "Digital Aristotle." The goal of that first step was to assess the state of the art in applied Knowledge Representation and Reasoning (KRR) by asking AI experts to represent 70 pages from the advanced placement (AP) chemistry syllabus and to deliver knowledge-based systems capable of answering questions from that syllabus. This paper reports the next step toward realizing a Digital Aristotle: we present the design and evaluation results for a system called AURA, which enables domain experts in physics, chemistry, and biology to author a knowledge base and that then allows a different set of users to ask novel questions against that knowledge base. These results represent a substantial advance over what we reported in 2004, both in the breadth of covered subjects and in the provision of sophisticated technologies in knowledge representation and reasoning, natural language processing, and question answering to domain experts and novice users.

Information extraction (IE) can identify a set of relations from free text to support question answering (QA). Until recently, IE systems were domain-specific and needed a combination of manual engineering and supervised learning to adapt to each target domain. A new paradigm, Open IE operates on large text corpora without any manual tagging of relations, and indeed without any pre-specified relations. Due to its open-domain and open-relation nature, Open IE is purely textual and is unable to relate the surface forms to an ontology, if known in advance. We explore the steps needed to adapt Open IE to a domain-specific ontology and demonstrate our approach of mapping domain-independent tuples to an ontology using domains from DARPA’s Machine Reading Project. Our system achieves precision over 0.90 from as few as 8 training examples for an NFL-scoring domain.

Actors in realistic social networks play not one but a number of diverse roles depending on whom they interact with, and a large number of such role-specific interactions collectively determine social communities and their organizations. Methods for analyzing social networks should capture these multi-faceted role-specific interactions, and, more interestingly, discover the latent organization or hierarchy of social communities. We propose a hierarchical Mixed Membership Stochastic Blockmodel to model the generation of hierarchies in social communities, selective membership of actors to subsets of these communities, and the resultant networks due to within- and cross-community interactions. Furthermore, to automatically discover these latent structures from social networks, we develop a Gibbs sampling algorithm for our model. We conduct extensive validation of our model using synthetic networks, and demonstrate the utility of our model in real-world datasets such as predator-prey networks and citation networks.

We revisit the problem of feature selection in linear discriminant analysis (LDA), that is, when features are correlated. First, we introduce a pooled centroids formulation of the multiclass LDA predictor function, in which the relative weights of Mahalanobis-transformed predictors are given by correlation-adjusted $t$-scores (cat scores). Second, for feature selection we propose thresholding cat scores by controlling false nondiscovery rates (FNDR). Third, training of the classifier is based on James--Stein shrinkage estimates of correlations and variances, where regularization parameters are chosen analytically without resampling. Overall, this results in an effective and computationally inexpensive framework for high-dimensional prediction with natural feature selection. The proposed shrinkage discriminant procedures are implemented in the R package ``sda'' available from the R repository CRAN.

In recent years, ideas from statistics and scientific computing have begun to interact in increasingly sophisticated and fruitful ways with ideas from computer science and the theory of algorithms to aid in the development of improved worst-case algorithms that are useful for large-scale scientific and Internet data analysis problems. In this chapter, I will describe two recent examples---one having to do with selecting good columns or features from a (DNA Single Nucleotide Polymorphism) data matrix, and the other having to do with selecting good clusters or communities from a data graph (representing a social or information network)---that drew on ideas from both areas and that may serve as a model for exploiting complementary algorithmic and statistical perspectives in order to solve applied large-scale data analysis problems.