This paper describes our ongoing effort to build an empathizing and adaptive storyteller system. The system under development aims to utilize emotional expressions generated from an avatar or a humanoid robot in addition to the listener’s responses which are monitored in real time, in order to deliver a story in an effective manner. We conducted a pilot study and the results were analyzed in two ways: first, through a survey questionnaire analysis based on the participant’s subjective ratings; second, through automated video analysis based on the participant’s emotional facial expression and eye blinking. The survey questionnaire results show that male participants have a tendency of more empathizing with a story character when a virtual storyteller is present, as compared to audio-only narration. The video analysis results show that the number of eye blinking of the participants is thought to be reciprocal to their attention.

The Intentional Fast-Forward (IFF) planner is an attempt to apply fast forward-chaining state-space search methods to intentional planning---planning such that every action is directed toward some character's goal. The IFF heuristic is based on Hoffmann's original Fast Forward heuristic (2001), which solves a simplified version of the problem and uses that solution as a guide for the real problem. IFF incorporates constraints imposed by intentional planning to narrow down the set of steps which can be taken next, and it identifies fruitless branches of the search space early.

The problem of optimal planning under uncertainty in collaborative multi-agent domains is known to be deeply intractable but still demands a solution. This thesis will explore principled approximation methods that yield tractable approaches to planning for AI assistants, which allow them to understand the intentions of humans and help them achieve their goals. AI assistants are ubiquitous in video games, mak- ing them attractive domains for applying these planning techniques. However, games are also challenging domains, typically having very large state spaces and long planning horizons. The approaches in this thesis will leverage recent advances in Monte-Carlo search, approximation of stochastic dynamics by deterministic dynamics, and hierarchical action representation, to handle domains that are too complex for existing state of the art planners. These planning techniques will be demonstrated across a range of video game domains.

Narrative Intelligence (NI) can help computational systems interact with users, such as through story generation, interactive narratives, and believable virtual characters. However, existing NI techniques generally require manually coded domain knowledge, restricting their scalability. An approach that intelligently, automatically and economically acquires script-like knowledge in any domain with strategic crowdsourcing will ease this bottleneck and broaden the application territory of narrative intelligence. This doctoral consortium paper defines the research problem, describes its significance, proposes a feasible research plan towards a Ph.D. dissertation, and reports on its current progress.

We describe ANGELINA3, a system that can automatically develop games along a defined theme, by selecting appropriate multimedia content from a variety of sources and incorporating it into a game's design. We discuss these capabilities in the context of the FACE model for assessing progress in the building of creative systems, and discuss how ANGELINA3 can be improved through further work.

We present POMCoP, a system for online planning in collaborative domains that reasons about how its actions will affect its understanding of human intentions, and demonstrate its use in building sidekicks for cooperative games. POMCoP plans in belief space. It explicitly represents its uncertainty about the intentions of its human ally, and plans actions which reveal those intentions or hedge against its uncertainty. This allows POMCoP to reason about the usefulness of incorporating information gathering actions into its plans, such as asking questions, or simply waiting to let humans reveal their intentions. We demonstrate POMCoP by constructing a sidekick for a cooperative pursuit game, and evaluate its effectiveness relative to MDP-based techniques that plan in state space, rather than belief space.

Automating parts of game creation benefits both professional and amateur game designers and much previous work has already made progress on this front. In this paper we tackle automating level design. We describe a general graph-based representation for game levels and present a preliminary system that leverages this representation. Our system automatically explores existing levels of a 2D platform game using the rapidly-exploring random tree (RRT) algorithm and constructs a compact graph representation from this exploration. Our system can also modify a graph representation on-the-fly to reflect user-directed changes to the existing level structure. This work constitutes an initial step toward the larger goal of automating level design in a general way.

Feature selection is a technique to screen out less important features. Many existing supervised feature selection algorithms use redundancy and relevancy as the main criteria to select features. However, feature interaction, potentially a key characteristic in real-world problems, has not received much attention. As an attempt to take feature interaction into account, we propose L1-LSMI, an L1-regularization based algorithm that maximizes a squared-loss variant of mutual information between selected features and outputs. Numerical results show that L1-LSMI performs well in handling redundancy, detecting non-linear dependency, and considering feature interaction.

This paper provides a generic framework of component analysis (CA) methods introducing a new expression for scatter matrices and Gram matrices, called Generalized Pairwise Expression (GPE). This expression is quite compact but highly powerful: The framework includes not only (1) the standard CA methods but also (2) several regularization techniques, (3) weighted extensions, (4) some clustering methods, and (5) their semi-supervised extensions. This paper also presents quite a simple methodology for designing a desired CA method from the proposed framework: Adopting the known GPEs as templates, and generating a new method by combining these templates appropriately.

The CUR matrix decomposition is an important extension of Nystr\"{o}m approximation to a general matrix. It approximates any data matrix in terms of a small number of its columns and rows. In this paper we propose a novel randomized CUR algorithm with an expected relative-error bound. The proposed algorithm has the advantages over the existing relative-error CUR algorithms that it possesses tighter theoretical bound and lower time complexity, and that it can avoid maintaining the whole data matrix in main memory. Finally, experiments on several real-world datasets demonstrate significant improvement over the existing relative-error algorithms.