Probabilistic graphical models, such as Bayesian networks, are intuitive and theoretically sound tools for modeling uncertainty. A major problem with applying Bayesian networks in practice is that it is hard to judge whether a model fits well a case that it is supposed to solve. One way of expressing a possible dissonance between a model and a case is the {\em surprise index}, proposed by Habbema, which expresses the degree of surprise by the evidence given the model. While this measure reflects the intuition that the probability of a case should be judged in the context of a model, it is computationally intractable. In this paper, we propose an efficient way of approximating the surprise index.

The updating is accomplished using Bayes'

How can robots fluently communicate with humans and have context-preserving conversation? It is the most momentous and crucial problem in robotics research, especially for service robots such as child-care robots. Here, we aim to develop a situated conversation system for child-care robots. The conversation system considers the current context between robots and children as well as the situation the child is in. The system consists of two parts. The first part tries to understand the context. This part uses the embedded sensors of robots to understand the context and wearable sensors of the child for getting information of the situation the child is in. The second part is to generate the situated conversation. In terms of the model, we designed a hierarchical Bayesian Network for the first part and a Hypernetwork model is used for the second part. We illustrate the application of communication with a child in a child-care service robots scenario. For this application, we collect wearable sensors’ data from the child and mother-child conversation data in daily life. Finally, we discuss our results and future works.

Procedural Content Generation (PCG) using machine learning is a fast growing area of research. Action Role Playing Game (ARPG) levels represent an interesting challenge for PCG due to their multi-tiered structure and nonlinearity. Previous work has used Bayes Nets (BN) to learn properties of the topological structure of levels from The Legend of Zelda. In this paper we describe a method for sampling these learned distributions to generate valid, playable level topologies. We carry this deeper and learn a sampleable representation of the individual rooms using Principal Component Analysis. We combine the two techniques and present a multi-scale machine learned technique for procedurally generating ARPG levels from a corpus of levels from The Legend of Zelda.

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.

Figure Crowdsourcing systems are commonly faced with the challenge 1 (a) shows the interface which allows researchers to set of making online decisions by assigning tasks to workers up experiments which run multiple active learning strategies in order to maximise accuracy while also minimising over a single dataset. Using this dialog, the user can construct cost. To aid researchers to reproduce, benchmark and extend an active learning strategy by combining an aggregation state-of-the-art active learning methods for crowdsourcing model, a task selection method and a worker selection systems, we developed the open-source.NET ActiveCrowd-method. The user can also select the number of judgements Toolkit.

Smart decision making at the tactical level is important for Artificial Intelligence (AI) agents to perform well in the domain of real-time strategy (RTS) games. Winning battles is crucial in RTS games, and while humans can decide when and how to attack based on their experience, it is challenging for AI agents to estimate combat outcomes accurately. A few existing models address this problem in the game of StarCraft but present many restrictions, such as not modeling injured units, supporting only a small number of unit types, or being able to predict the winner of a fight but not the remaining army. Prediction using simulations is a popular method, but generally slow and requires extensive coding to model the game engine accurately. This paper introduces a model based on Lanchester's attrition laws which addresses the mentioned limitations while being faster than running simulations. Unit strength values are learned using maximum likelihood estimation from past recorded battles. We present experiments that use a StarCraft simulator for generating battles for both training and testing, and show that the model is capable of making accurate predictions. Furthermore, we implemented our method in a StarCraft bot that uses either this or traditional simulations to decide when to attack or to retreat. We present tournament results (against top bots from 2014 AIIDE competition) comparing the performances of the two versions, and show increased winning percentages for our method.

Current crowdsourcing platforms such as Amazon Mechanical Turk provide an attractive solution for processing of high-volume tasks at low cost. However, problems of quality control remain a major concern. We developed a private crowdsourcing system (PCSS) running in a intranetwork, that allow us to devise for quality control methods. In the present work, we designed a novel task allocation method to improve accuracy of task results in PCSS. PCSS analyzed relations between tasks from workers' behavior using Bayesian network, then created learning tasks according to analyzed relations. PCSS increased quality of task results by allocating learning tasks to workers before processing difficult tasks. PCSS created 8 learning tasks automatically for 2 target task categories and increased accuracy of task results by 10.77 point on average. We found that creating learning tasks according to analyzed relations is a practical method to improve the quality of workers.

Models for aggregating contributions by crowd workers have been shown to be challenged by the rise of task-specific biases or errors. Task-dependent errors in assessment may shift the majority opinion of even large numbers of workers to an incorrect answer. We introduce and evaluate probabilistic models that can detect and correct task-dependent bias automatically. First, we show how to build and use probabilistic graphical models for jointly modeling task features, workers' biases, worker contributions and ground truth answers of tasks so that task-dependent bias can be corrected. Second, we show how the approach can perform a type of transfer learning among workers to address the issue of annotation sparsity. We evaluate the models with varying complexity on a large data set collected from a citizen science project and show that the models are effective at correcting the task-dependent worker bias. Finally, we investigate the use of active learning to guide the acquisition of expert assessments to enable automatic detection and correction of worker bias.

In this paper, we propose a model-based clustering method (TVClust) that robustly incorporates noisy side information as soft-constraints and aims to seek a consensus between side information and the observed data. Our method is based on a nonparametric Bayesian hierarchical model that combines the probabilistic model for the data instance and the one for the side-information. An efficient Gibbs sampling algorithm is proposed for posterior inference. Using the small-variance asymptotics of our probabilistic model, we then derive a new deterministic clustering algorithm (RDP-means). It can be viewed as an extension of K-means that allows for the inclusion of side information and has the additional property that the number of clusters does not need to be specified a priori. Empirical studies have been carried out to compare our work with many constrained clustering algorithms from the literature on both a variety of data sets and under a variety of conditions such as using noisy side information and erroneous k values. The results of our experiments show strong results for our probabilistic and deterministic approaches under these conditions when compared to other algorithms in the literature.