Cinematic, Ambient, Inhabitable Narrative Environments (CAINEs) are conceptual AI-driven interactive story systems combining text, audio, and visual imagery that are scalable and adaptable to a wide range of storytelling needs and interactor inputs. Conceived by at artist outside the AI community, they represent an opportunity to use AI in a nontraditional and immersive narrative fashion that relies not on the goal-based arrangement of story elements, but on the accretion and association of those elements in the minds of interactors. This paper represents the initial phase of the project’s development.

We develop a flexible reward plan to elicit truthful predictive probability distribution over a set of uncertain events from workers.  In our reward plan, the principal can assign rewards for incorrect predictions according to her similarity between events.  In the spherical proper scoring rule, a worker's expected utility is represented as the inner product of her truthful predictive probability and her declared probability. We generalize the inner product by introducing a reward matrix that defines a reward for each prediction-outcome pair. We show that if the reward matrix is symmetric and positive definite, the spherical proper scoring rule guarantees the maximization of a worker's expected utility when she truthfully declares her prediction.

Hence, they do not address mobile resource devices, it has now become practically feasible to enable constraints (e.g., energy, bandwidth) and also result in unnecessary people to share information about dynamic events (e.g., trees spam. On the other hand, multi-cast approaches randomly fallen on roads due to a storm, sudden truck breakdowns send the queries to some of the users to preserve mobile and unscheduled processions) in their current location. This resources, but they do not ensure the direction of queries strongly motivates facilitation of various kinds of locationdependent to the most relevant users.

Plan synthesis often requires complete domain models and initial states as input. In many real world applications, it is difficult to build domain models and provide complete initial state beforehand. In this paper we propose to turn to the crowd for help before planning. We assume there are annotators available to provide information needed for building domain models and initial states. However, there might be a substantial amount of discrepancy within the inputs from the crowd. It is thus challenging to address the planning problem with possibly noisy information provided by the crowd. We address the problem by two phases. We first build a set of Human Intelligence Tasks (HITs), and collect values from the crowd. We then estimate the actual values of variables and feed the values to a planner to solve the problem.

We consider a finite-pool data categorization scenario which requires exhaustively classifying a given set of examples with a limited budget. We adopt a hybrid human-machine approach which blends automatic machine learning with human labeling across a tiered workforce composed of domain experts and crowd workers. To effectively achieve high-accuracy labels over the instances in the pool at minimal cost, we develop a novel approach based on decision-theoretic active learning. On the important task of biomedical citation screening for systematic reviews, results on real data show that our method achieves consistent improvements over baseline strategies. To foster further research by others, we have made our data available online.

This paper proposes an online transfer framework to capture the interaction among agents and shows that current transfer learning in reinforcement learning is a special case of online transfer. Furthermore, this paper re-characterizes existing agents-teaching-agents methods as online transfer and analyze one such teaching method in three ways. First, the convergence of Q-learning and Sarsa with tabular representation with a finite budget is proven. Second, the convergence of Q-learning and Sarsa with linear function approximation is established. Third, the we show the asymptotic performance cannot be hurt through teaching. Additionally, all theoretical results are empirically validated.

In this paper, we present the conceptual model of a realworld application of Markov Decision Processes to dam management. The idea is to demonstrate that it is possible to efficiently automate the construction of operation policies by modelling the problem as a sequential decision problem that can be easily solved using stochastic dynamic programming. We will explain the problem domain and provide an analysis of the resulting value and policy functions. We will also present a useful discussion about the issues that will appear when the conceptual model to be extended into a real-world application.

This work presents a general-purpose character reasoning model intended for usage by autonomous team actors that are acting as believable characters (e.g., human team actors fall into this category). The idea is that selecting a cast of believable characters can predetermine a solution to an unexpected challenge that the team may be facing in a rescue mission. This approach in certain cases proves more efficient than an alternative approach based on rational decision making and planning, which ignores the question of character believability. This point is illustrated with a simple numerical example in a virtual world paradigm.

With advancements in technology, smartphones can already serve as memory aids. Electronic calendars are of great use in time-based memory tasks. In this project, we enter the mixed reality realm for helping dementia patients. Dementia is a general term for a decline in mental ability severe enough to interfere with daily life. Memory loss is an example. Here, mixed reality refers to the merging of real and virtual worlds to produce new episodic memory visualisations where physical and digital objects co-exist and interact in real-time. Cognitive models are approximations of a patient's mental abilities and limitations involving conscious mental activities (such as thinking, understanding, learning, and remembering). External representations of episodic memory help patients and caregivers coordinate their actions with one another. We advocate distributed cognition, which involves the coordination between individuals, artefacts and the environment, in four main implementations of artificial intelligence technology in the Kognit storyboard: (1) speech dialogue and episodic memory retrieval; (2) monitoring medication management and tracking an elder's behaviour (e.g., drinking water); (3) eye tracking and modelling cognitive abilities; and (4) serious game development towards active memory training. We discuss the storyboard, use cases and usage scenarios, and some implementation details of cognitive models and mixed reality hardware for the patient. The purpose of future studies is to determine the extent to which cognitive enhancement technology can be used to decrease caregiver burden.

Exploring web and in particular social media data is an essential task to many of the subject matter experts in order to discover content around their subject of interest. It is important to provide them with a tool to define their scope of vocabulary, i.e what to search for, and suggest them commonly used terms besides the serendipitous terms allowing them to define their scope of explorations. This paper presents methods on constructing ``domain models" which are families of keywords and extractors to enable focus on social media documents relevant to a project using multiple channels of information extraction.