Music composition can be a challenge for many small- to medium-sized game companies, largely due to the expense and difficulty in creating original music for each level of a game. To address this, we developed a tool that automatically generates original music, by training a music generator on pieces whose style the game designer wishes to imitate. The generator then creates original music in that style in real-time, and switches between styles when signaled by the game. This software has been refined to produce music that is coherent and imitates a composer’s larger music structure.

Narratives are powerful because of their impact on our emotional experiences. Recent years have witnessed significant advances in affective computing and intelligent interaction, presenting a broad range of opportunities for enhancing the design, implementation, and adaptivity of interactive narratives. This paper presents preliminary work examining story-centric games and interactive narratives from the perspective of psychological theories of emotion, with a particular focus on player affect. We examine the sources and duration of player emotion, social facets of emotion, players’ individual differences in emotion, and meta-emotions. Recommendations and future directions for research on player emotion in interactive narratives are discussed.

This paper describes a new candidate method of computing autonomous "self confidence." We describe how to analyze a plan for possible but unexpected break down cases and how to adapt the plan to circumvent those conditions. We view the result plan as more stable than the original one. The ability of achieving such plan stability is the core of how we propose to compute a system’s self confidence in its decisions and plans. This paper summarizes this approach and presents a preliminary evaluation that shows our approach is promising.

We describe a cognitive assistant in early-stage development for the United States Air Force as an aid to contracting officers and potential commercial offerors for navigating the government-contracting process. The goal is easing compliance and affording flexibility and transparency so as to support an innovative and rapid acquisition process. The motivation, use cases, and technical approach for MICA, a Machine Interface for Contracting Assistance, are discussed here along with the technical challenges posed.

Today’s businesses, government and society work and services are centered around interactions, collaborations and knowledge work. The pace, amount and veracity of data generated and processed by a worker has accelerated significantly to the level that challenged human cognitive load and productivity. On the other hand, big data has provided an unprecedented opportunity for AI to tackle one of the main challenges hindering the AI progress: building models of world in a scalable, adaptive and dynamic manner. In this paper, we describe the technology requirements of building cognitive assistance technologies that assists human workers, and present a cognitive work assistant framework that aims at offering intelligence assistance to workers to improve their productivity and agility. We then describe the design and development of a set of cognitive services offered by the framework, based on advanced NLP and machine learning methods. The cognitive services help workers in processing and linking information and identifying and tracking work items over interactions in communication channels such as email, social conversations and media, chats and messaging and calendar applications. These cognitive services are designed to be adaptive, online and personalized so that over time adapt to changing environment and knowledge, and the models become personalized through learning preferences and working language and style of the subject worker.

In this paper we describe a hierarchical method for procedurally generating 2D game maps using multi-dimensional Markov chains (MdMCs). Our method takes a collection of 2D game maps, breaks them into small chunks and performs clustering to find a set of chunks that correspond to high-level structures (high-level tiles) in the training maps. This set of high-level tiles is then used to re-represent the training maps, and to fit two sets of MdMC models: a high-level model captures the distribution of high-level tiles in the map, and a set of low-level models capture the internal structure of each high-level tile. These two sets of models can then be used to hierarchically generate new maps. We test our approach using two classic games, Super Mario Bros. and Loderunner, and compare the results against other existing map generators.

This paper describes entries to the third Playable Experiences track to be held at the AIIDE conference. We discuss the five entries accepted to the track for 2015, as well as the ongoing development of the track as part of AIIDE.

We present Fiascomatic , a mixed initiative system for generating consistent scenarios for the indie storytelling RPG Fiasco . Players can repeatedly generate scenarios, locking down aspects of a scenario they like and regenerating aspects they don’t, until they arrive at a scenario they find entertaining.  It is not a story generation system; it generates scenarios from which players then generate stories.  Nor is it intended to generate optimal scenarios; it generates random scenarios which the players can then curate according to their taste. Fiascomatic presents an interesting intermediate point between non-automated table-top RPGs and fully automated systems such as story generators or autonomous characters.  It is a tool that can be used by Fiasco players to speed the generation of game setups while preserving creative input on the part of the players, and by Fiasco playset authors to make automated playsets.

MKULTRA is an experimental game that explores novel AI-based game mechanics. Similar in some ways to text-based interactive fiction, the player controls a character who interacts with other characters through dialog.  Unlike traditional IF, MKULTRA characters have simple natural language understanding and generation capabilities, sufficient to answer questions and carry out simple tasks.  The game explores a novel game mechanic, belief injection, in which players can manipulate the behavior of NPCs by injecting false beliefs into their knowledge bases.  This allows for an unusual form of puzzle-based gameplay, in which the player must understand the beliefs and motivational structure of the characters well enough to understand what beliefs to inject.

We sketch the process of creating a novel video game by blending two video games specified in the Video Game Description Language (VGDL), following the COINVENT computational model of conceptual blending. We highlight the choices that need to be made in this process, and discuss the prospects for a computational game designer based on blending.