Abstract. Game generation systems perform automated, intelligent design of games (i.e. videogames, boardgames), reasoning about both the abstract rule system of the game and the visual realization of these rules. Although, as an instance of the problem of creative design, game generation shares some common research themes with other creative AI systems such as story and art generators, game generation extends such work by having to reason about dynamic, playable artifacts. Like AI work on creativity in other domains, work on game generation sheds light on the human game design process, offering opportunities to make explicit the tacit knowledge involved in game design and test game design theories. Finally, game generation enables new game genres which are radically customized to specific players or situations; notable examples are cell phone games customized for particular users and newsgames providing commentary on current events. We describe an approach to formalizing game mechanics and generating games using those mechanics, using WordNet and ConceptNet to assist in performing common-sense reasoning about game verbs and nouns. Finally, we demonstrate and describe in detail a prototype that designs micro-games in the style of Nintendo’s WarioWare series. 1

We present an architecture for complex affective robots for human-robot interaction. After describing our rationale for using affect as a means of “architectural integration”, we give a quick conceptual example of how affect can play an organizational role in a complex agent and then describe our proposed affective architecture, its functionality and implementation, and results from experimental evaluations on an autonomous robot.

Abstract — Robots that can interact naturally with humans require the integration and coordination of many different components with heavy computational demands. We argue that an architecture framework with facilities for dynamic, reliable, faultrecovering, remotely accessible, distributed computing is needed for the development and operation of applications that support and enhance human activities and capabilities. We describe a robotic architecture development system, called ADE, that is built on top of a multi-agent system in order to provide all of the above features. Specifically, we discuss support for autonomic computing in ADE, briefly comparing it to related features of other commonly used robotic systems. We also report our experiences with ADE in the development of an architecture for an intelligent robot assistant and provide experimental results demonstrating the system’s utility. I.

In a story telling authoring task, an author often wants to set up meaningful connections between different media, such as between a text and photographs. To facilitate this task, it is helpful to have a software agent dynamically adapt the presentation of a media database to the user's authoring activities, and look for opportunities for annotation and retrieval. Expecting the user to manually annotate photos with keywords greatly burdens the user. Furthermore, even when photos are properly annotated, their retrieval is often very brittle because semantic connections between annotations and the story text that are "obvious" to people (e.g. between “bride” and “wedding”) may easily be missed by the computer. ARIA (Annotation and Retrieval Integration Agent) is a software agent that acts as an assistant to a user writing e-mail or Web pages. As the user types a story, it does continuous retrieval and ranking on a photo database. It can use descriptions in the story text to semi-automatically annotate pictures based on how they are used. The focus of this thesis is threefold: Improving ARIA’s automated annotation capabilities through world-aware semantic understanding of the text; making photo retrieval more robust by using a commonsense knowledge base, Open Mind Commonsense, to make semantic connections between the story text and annotations (e.g. connect "bride" and "wedding"); and learning personal commonsense through the text (e.g. “My sister’s name is Mary.”) that can then be used to improve photo retrieval by enabling personalized semantic connections.

In this paper we describe a novel methodology for retrieving and combining information from multiple ontologies. In the last decades the number and diversity of available ontologies has grown considerably. There may be a variety of such resources available, but the cost to integrate them into an application is incremental, and often prohibitive for exploratory prototyping and discouraging for larger-scale integration. However, a unified cross-ontology query interface provides the capability for searching them independently and combining results. Beyond a common interface we further explore combining ontological graph search across knowledge bases and demonstrate superior results in the context of type-checking for automated Q&A systems. The paper presents the method and our initial promising results. 1

Game-design novices increasingly hope to use game-like expression as a way to express content such as opinions and educational material. Existing game-design toolkits such as Game Maker ease the programming burden, bringing the design of small games within the technical reach of lowbudget, non-expert groups. The design process itself remains a roadblock, however: It is not at all obvious how to present topics such as political viewpoints or bike safety in a way that makes use of the unique qualities of the interactive game medium. There are no tools to assist in that aspect of the game design process, and as a result virtually all expressive games come from a small number of game studios founded by experts in designing such games. We propose a game-design assistant that acts in a mixed-initiative fashion, helping the author understand the content of her design-inprogress, providing suggestions or automating the process where possible, and even offering the possibility for parts of the game to be dynamically generated at runtime in response to player interaction. We describe a prototype system that interactively helps authors define spaces of games in terms of common-sense constraints on their real-world references, provides support for them to understand and iteratively refine such spaces, and realizes specific games from the spaces as playable mobile-phone games in response to user input.

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Open Mind Commons is an interface for collecting common sense knowledge from users over the Web. By giving the user many forms of feedback and using inferences by analogy to find appropriate questions to ask, Open Mind Commons can learn well-connected structures of common sense knowledge, refine its existing knowledge, and build analogies that lead to even more powerful inferences. ACM Classification: H.3.3 [INFORMATION STORAGE

In this thesis, I present a system for reasoning with common sense knowledge in multiple natural languages, as part of the Open Mind Common Sense project. The knowledge that Open Mind collects from volunteer contributors is represented as a semantic network called ConceptNet. Using principal component analysis on the graph structure of ConceptNet yields AnalogySpace, a vector space representation of common sense knowledge. This representation reveals large-scale patterns in the data, while smoothing over noise, and predicts new knowledge that the database should contain. The inferred knowledge, which a user survey shows is often correct, is used as part of a feedback loop that shows contributors what the system is learning and guides them to contribute useful new knowledge.

Hybrid architectures have been developed to preserve the responsiveness of reactive layers while also providing the benefits of higher-level deliberative capabilities. The challenge of hybrid architecture design is to integrate layers that operate on different time scales, use different notions of spatial relations, maintain varying degrees of state, etc. Techniques for integration, however, are typically very specific to the application or particular hybrid architecture. A general method is proposed for constructing hybrid architectures via the integration of pre-existing reactive and deliberate layers that requires minimal modifications. Its potential is demonstrated in navigation experiments on a robot, achieving performance comparable to a commonly used hybrid system with specially designed layers.