In order to assist scriptwriters during the process of story-writing, we have developed a system that can extract information from natural language stories, and allow for story-centric as well as character-centric reasoning. These inferencing capabilities are exposed to the user through intuitive querying systems, allowing the scriptwriter to ask the system questions about story and character information. We introduce knowledge bytes as atoms of information and demonstrate that the system can parse text into a stream of knowledge bytes and use these mentioned reasoning capabilities through logical reasoning.

A live interactive narrative (LIN) is an experience where multiple players take on fictional roles and interact with real-world objects and actors to participate in a pre-authored narrative. Temporal properties of LINs are important to its viability and aesthetic quality and hence deserve special design consideration. In this paper, we tackle the largely overlooked problem of scheduling a multiplayer interactive narrative and propose the Live Interactive Narrative Scheduling Problem (LINSP), which handles reasoning under temporal uncertainty, resource scheduling, and non-linear plot choices. We present a mixed-integer linear programming formulation of the problem and empirically evaluates its scalability over large narrative instances.

When the underlying assumptions of commonality of purpose and content break down, the interaction does as well. A great deal of the art of interaction design lies in minimizing what is, from the agent's point of view, out-of-task behavior, both by anticipating natural intask communication and by providing cues to lead participants down the predicted paths. Anticipation and cueing are particularly important in designing interactions for young children, a population that is limited in its ability to understand and adapt to the bounds of a system when things go awry. Most speech and natural language research that focuses on this population has pedagogy (Ogan et al. 2012; Gordon and Breazeal 2015) or therapy As explained briefly by Edith, there are two main game actions: effecting a change to the model by naming one of the clothing items or accessories on the board, and requesting a picture of the increasingly crazily clad model to be printed and taken home afterward. The majority of the interaction consists of 20 choice cycles during each of which a valid reference to a board item is made, the model changes, and a replacement item appears.

Automatic analysis of game levels can provide as- sistance to game designers and procedural content generation. We introduce a static-dynamic scale to categorize level analysis strategies, which captures the extent that the analysis depends on player simulation. Due to its ability to automatically learn intermediate representations for the task, a convolutional neural network (CNN) provides a general tool for both types of analysis. In this paper, we explore the use of CNN to analyze 1,437 Infinite Mario levels. We further propose a deep reinforcement learning technique for dynamic analysis, which allows the simulated player to pay a penalty to reduce error in its control. We empirically demonstrate the effectiveness of our techniques and complementarity of dynamic and static analysis.

The workshop program at the Eleventh Annual AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment was held November 14–15, 2015 at the University of California, Santa Cruz, USA. The program included 4 workshops (one of which was a joint workshop): Artificial Intelligence in Adversarial Real-Time Games, Experimental AI in Games, Intelligent Narrative Technologies and Social Believability in Games, and Player Modeling. This article contains the reports of three of the four workshops.

Viewpoint estimation, especially in case of multiple object classes, remains an important and challenging problem. First, objects under different views undergo extreme appearance variations, often making within-class variance larger than between-class variance. Second, obtaining precise ground truth for real-world images, necessary for training supervised viewpoint estimation models, is extremely difficult and time consuming. As a result, annotated data is often available only for a limited number of classes. Hence it is desirable to share viewpoint information across classes. Additional complexity arises from unaligned pose labels between classes, i.e. a side view of a car might look more like a frontal view of a toaster, than its side view. To address these problems, we propose a metric learning approach for joint class prediction and pose estimation. Our approach allows to circumvent the problem of viewpoint alignment across multiple classes, and does not require dense viewpoint labels. Moreover, we show, that the learned metric generalizes to new classes, for which the pose labels are not available, and therefore makes it possible to use only partially annotated training sets, relying on the intrinsic similarities in the viewpoint manifolds. We evaluate our approach on two challenging multi-class datasets, 3DObjects and PASCAL3D+.

Buoyed by the success of deep multilayer neural networks, there is renewed interest in scalable learning of Bayesian neural networks. Here, we study algorithms that utilize recent advances in Bayesian inference to efficiently learn distributions over network weights. In particular, we focus on recently proposed assumed density filtering based methods for learning Bayesian neural networks -- Expectation and Probabilistic backpropagation. Apart from scaling to large datasets, these techniques seamlessly deal with non-differentiable activation functions and provide parameter (learning rate, momentum) free learning. In this paper, we first rigorously compare the two algorithms and in the process develop several extensions, including a version of EBP for continuous regression problems and a PBP variant for binary classification. Next, we extend both algorithms to deal with multiclass classification and count regression problems. On a variety of diverse real world benchmarks, we find our extensions to be effective, achieving results competitive with the state-of-the-art.

In this paper we consider optimization as an approach for quickly and flexibly developing hybrid cognitive capabilities that are efficient, scalable, and can exploit knowledge to improve solution speed and quality. In this context, we focus on the Three-Weight Algorithm, which aims to solve general optimization problems. We propose novel methods by which to integrate knowledge with this algorithm to improve expressiveness, efficiency, and scaling, and demonstrate these techniques on two example problems (Sudoku and circle packing).

We present a framework to represent and reason about nar- ratives. We build a symbolic probabilistic representation of the temporal sequence of world states and events implied by a narrative using statistical approaches. We show that the combination of this representation together with domain knowledge and symbolic probabilistic reasoning algorithms enables understanding of a narrative and answering semantic questions whose responses are not contained in the narrative. In our experiments, we show the power of our framework (vs. traditional approaches) in answering semantic questions for two domains of RoboCup soccer commentaries and early reader children stories focused on spatial contexts.