IBM Research undertook a challenge to build a computer system that could compete at the human champion level in real time on the American TV quiz show, Jeopardy. The extent of the challenge includes fielding a real-time automatic contestant on the show, not merely a laboratory exercise. The Jeopardy Challenge helped us address requirements that led to the design of the DeepQA architecture and the implementation of Watson. After three years of intense research and development by a core team of about 20 researchers, Watson is performing at human expert levels in terms of precision, confidence, and speed at the Jeopardy quiz show. Our results strongly suggest that DeepQA is an effective and extensible architecture that can be used as a foundation for combining, deploying, evaluating, and advancing a wide range of algorithmic techniques to rapidly advance the field of question answering (QA).

In this paper, we present the Procedural Game Adaptation (PGA) framework: a designer-controlled way to adapt the Changing the dynamics of a video game (i.e., how the dynamics of a given video game during end-user play. When player's actions affect the game world) is a fundamental tool implemented, this framework produces a deterministic, online of video game design. In Pac-Man, eating a power pill allows adaptation agent (called an experience manager (Riedl the player to temporarily defeat the ghosts that pursue et al. 2011)) that automatically performs two tasks: 1) it and threaten her for the vast majority of the game; in Call gathers information about a game's current player, 2) it of Duty 4, taking the perk called "Deep Impact" allows the uses that information to estimate which of several different player's bullets to pass through certain walls without being changes to the game's dynamics will maximize some playerspecific stopped. The parameters of such changes (e.g., how much value (e.g., fun, sense of influence, etc.). the ghosts slow down while vulnerable) are usually determined by the game's designers long before its release, with

Around the same time, a system called Player-Specific From Shakespeare's "Romeo and Juliet" to George Lucas' Stories via Automatically Generated Events (PaSSAGE) "Star Wars" to BioWare's "Jade Empire" to campfire stories (Thue et al. 2007) was proposed, which used AI techniques to baseball commentary, story-telling is a fundamental to model the player as he/she experiences a narrative-rich part of entertainment. A strong narrative resonates with our video game. Such a continuously updated player model was minds, hearts and souls and keeps us engaged. We remember used to dynamically adapt the story, tailoring it to the current the stories of our childhood and retell them to our own player. Unlike, ASD, PaSSAGE did not have any automation children. Story-telling has delighted and saddened the human at the design stage and relied on a human designer to race since the beginning of time and shows no signs of foresee all possible ways of a player breaking the story and slowing down. But can it be improved with technology?

In the context of interactive, virtual experiences, the use of personality models to maintain consistent character behaviour is becoming more widespread in both industry and academia. Most current techniques, however, are limited in one of three ways: either they overly restrict user actions, have a high cost for creating varied content, or rely on a representation that prohibits conveying complex content to the user.  Toward addressing these issues, we introduce Socially Consistent Role Passing, a mechanism for ensuring consistent character behaviour that leverages the design of PaSSAGE, an existing system for generating adaptive, interactive stories.  While results from previous human user studies have shown that PaSSAGE improves the enjoyment of players with little gaming experience, we present results from a new study showing that PaSSAGE's adaptive stories, augmented with Socially Consistent Role Passing, improve the enjoyment of all players versus a set of fixed-structure alternatives.