Chinese Dark Chess, a nondeterministic two-player game, has not been studied thoroughly. State-of-the-art programs focus on using search algorithms to explore the probability behavior of flipping unrevealed pieces in the opening and the midgame phases. There has been comparatively little research on opening books and endgame databases, especially endgames with nondeterministic flips. In this paper, we propose an equivalence relation that classifies the complex piece relations between the material combinations of each player, and derive a partition for all such material combinations. The technique can be applied to endgame database compression to reduce the number of endgames that need to be constructed.

Versu is a text-based simulationist interactive drama. Because it uses autonomous agents, the drama is highly replayable: you can play the same story from multiple perspectives, or assign different characters to the various roles. The architecture relies on the notion of a social practice to achieve coordination between the independent autonomous agents. A social practice describes a recurring social situation, and is a successor to the Schankian script. Social practices are implemented as reactive joint plans, providing affordances to the agents who participate in them.

The rise of free-to-play and other service-based business models in the online gaming market brought to game publishers problems usually associated to markets like mobile telecommunications and credit cards, especially customer churn. Predictive models have long been used to address this issue in these markets, where companies have a considerable amount of demographic, economic, and behavioral data about their customers, while online game publishers often only have behavioral data. Simple time series' feature representation schemes like RFM can provide reasonable predictive models solely based on online game players' login records, but maybe without fully exploring the predictive potential of these data. We propose a frequency analysis approach for feature representation from login records for churn prediction modeling. These entries (from real data) were converted into fixed-length data arrays using four different methods, and then these were used as input for training probabilistic classifiers with the k-nearest neighbors machine learning algorithm.

In this paper, we present a strategic bidding framework for repeated auctions with monitoring and entry fees. We motivate and formally define the desired properties of our framework and present a recursive bidding algorithm, according to which buyers learn to avoid submitting bids in stages where they have a relatively low chance of winning the auctioned item. The proposed bidding strategies are computationally simple as players do not need to recompute the sequential strategies from the data collected to date. Pursuing the proposed efficient bidding (EB) algorithm, players monitor their relative performance in the course of the game and submit their bids based on their current estimate of the market condition. We prove the stability and robustness of the proposed strategies and show that they dominate myopic and random bidding strategies using an experiment in search engine marketing.

The aim of general game playing (GGP) is to create programs capable of playing a wide range of different games at an expert level, given only the rules of the game. The most successful GGP programs currently employ simulation-based Monte Carlo tree search (MCTS). The performance of MCTS depends heavily on the simulation strategy used. In this paper, we introduce improved simulation strategies for GGP that we implement and test in the GGP agent CADIAPLAYER, which won the International GGP competition in both 2007 and 2008. There are two aspects to the improvements: first, we show that a simple?-greedy

When using brain-computer interfaces (BCIs) to control a game, the BCI may have to compete with gaming tasks for the same perceptual and cognitive resources. We investigated: 1) if and to what extent event-related potentials (ERPs) and ERP-BCI performance are affected in a dual-task situation; and 2) if these effects are an area function of the level of difficulty of a concurrent task. Ten participants performed an ERP-BCI task that involved attending to a target location in sequences of tactile stimuli. The ERP-BCI task was performed either in isolation or secondary to a visual n-back task with two levels of difficulty. We observed: 1) a decreased P300 and BCI bit rate, and an increased level of subjective mental effort for both dual-task conditions compared to the BCI-only condition; the decreased classification accuracies were still well above chance, but arguably too low for effective BCI control; and 2) we did not find an effect of task difficulty on the P300, bit rates, and subjective mental effort.

Current plan checking process has been a tedious and time consuming process for developers and local authorities with many errors and problems. This may result to a late of building development progress and can cost a big number amount of money. The aim of this research is to build a software information system that integrates development plan using computer-aided design (CAD) and to evaluate the development plan according to the standards. The compliance plan will be stored into knowledge-based repository for use by other local authorities for checking purposes. This building plan checking system (BPCS) will also acts as a repository for the development plan configuration standards in the data processing system.

Designing games that support knowledge and skill acquisition has become a promising frontier of education techniques, since games are able to capture the user concentration for long periods and can present users with realistic and compelling challenges. In this scenario, there is a need for scientific and engineering methods to build games not only as more realistic simulations of the physical world but as means to provide effective learning experiences. Abstracting state of the art serious games' (SGs) features, we propose a new design methodology for the sand box serious games (SBSGs) class, decoupling content from the delivery strategy during the gameplay. This methodology aims at making design more efficient and standardized in order to meet the growing demand for interactive learning. The methodology consists in modeling an SBSG as a hierarchy of tasks (e.g., missions) and specifies the requirements for a runtime scheduling policy that maximizes learning objectives in a full entertainment context.

Intelligent agents working in real-time domains need to adapt to changing circumstance so that they can improve their performance and avoid their mistakes. AI agents designed for interactive games, however, typically lack this ability. Game agents are traditionally implemented using static, hand-authored behaviors or scripts that are brittle to changing world dynamics and cause a break in player experience when they repeatedly fail. Furthermore, their static nature causes a lot of effort for the game designers as they have to think of all imaginable circumstances that can be encountered by the agent. The problem is exacerbated as state-of-the-art computer games have huge decision spaces, interactive users, and real-time performance that make the problem of creating AI approaches for these domains harder.

With characters in computer games and interactive media increasingly being based on real actors, the individuality of an actor's performance should not only be reflected in the appearance and animation of the character but also in the AI that governs the character's behavior and interactions with the environment. Machine learning methods applied to motion capture data provide a way of doing this. This paper presents a method for learning the parameters of a finite-state machine (FSM) controller. The method learns both the transition probabilities of the FSM and also how to select animations based on the current state.