119,089 research outputs found
Predicting In-game Actions from Interviews of NBA Players
Sports competitions are widely researched in computer and social science,
with the goal of understanding how players act under uncertainty. While there
is an abundance of computational work on player metrics prediction based on
past performance, very few attempts to incorporate out-of-game signals have
been made. Specifically, it was previously unclear whether linguistic signals
gathered from players' interviews can add information which does not appear in
performance metrics. To bridge that gap, we define text classification tasks of
predicting deviations from mean in NBA players' in-game actions, which are
associated with strategic choices, player behavior and risk, using their choice
of language prior to the game. We collected a dataset of transcripts from key
NBA players' pre-game interviews and their in-game performance metrics,
totalling in 5,226 interview-metric pairs. We design neural models for players'
action prediction based on increasingly more complex aspects of the language
signals in their open-ended interviews. Our models can make their predictions
based on the textual signal alone, or on a combination with signals from
past-performance metrics. Our text-based models outperform strong baselines
trained on performance metrics only, demonstrating the importance of language
usage for action prediction. Moreover, the models that employ both textual
input and past-performance metrics produced the best results. Finally, as
neural networks are notoriously difficult to interpret, we propose a method for
gaining further insight into what our models have learned. Particularly, we
present an LDA-based analysis, where we interpret model predictions in terms of
correlated topics. We find that our best performing textual model is most
associated with topics that are intuitively related to each prediction task and
that better models yield higher correlation with more informative topics.Comment: First two authors contributed equally. To be published in the
Computational Linguistics journal. Code is available at:
https://github.com/nadavo/moo
Shallow decision-making analysis in General Video Game Playing
The General Video Game AI competitions have been the testing ground for
several techniques for game playing, such as evolutionary computation
techniques, tree search algorithms, hyper heuristic based or knowledge based
algorithms. So far the metrics used to evaluate the performance of agents have
been win ratio, game score and length of games. In this paper we provide a
wider set of metrics and a comparison method for evaluating and comparing
agents. The metrics and the comparison method give shallow introspection into
the agent's decision making process and they can be applied to any agent
regardless of its algorithmic nature. In this work, the metrics and the
comparison method are used to measure the impact of the terms that compose a
tree policy of an MCTS based agent, comparing with several baseline agents. The
results clearly show how promising such general approach is and how it can be
useful to understand the behaviour of an AI agent, in particular, how the
comparison with baseline agents can help understanding the shape of the agent
decision landscape. The presented metrics and comparison method represent a
step toward to more descriptive ways of logging and analysing agent's
behaviours
An overview of recent research results and future research avenues using simulation studies in project management
This paper gives an overview of three simulation studies in dynamic project scheduling integrating baseline scheduling with risk analysis and project control. This integration is known in the literature as dynamic scheduling. An integrated project control method is presented using a project control simulation approach that combines the three topics into a single decision support system. The method makes use of Monte Carlo simulations and connects schedule risk analysis (SRA) with earned value management (EVM). A corrective action mechanism is added to the simulation model to measure the efficiency of two alternative project control methods. At the end of the paper, a summary of recent and state-of-the-art results is given, and directions for future research based on a new research study are presented
Group emotion modelling and the use of middleware for virtual crowds in video-games
In this paper we discuss the use of crowd
simulation in video-games to augment their realism. Using
previous works on emotion modelling and virtual crowds we
define a game world in an urban context. To achieve that, we
explore a biologically inspired human emotion model,
investigate the formation of groups in crowds, and examine
the use of physics middleware for crowds. Furthermore, we
assess the realism and computational performance of the
proposed approach. Our system runs at interactive frame-rate
and can generate large crowds which demonstrate complex
behaviour
Survey on Evaluation Methods for Dialogue Systems
In this paper we survey the methods and concepts developed for the evaluation
of dialogue systems. Evaluation is a crucial part during the development
process. Often, dialogue systems are evaluated by means of human evaluations
and questionnaires. However, this tends to be very cost and time intensive.
Thus, much work has been put into finding methods, which allow to reduce the
involvement of human labour. In this survey, we present the main concepts and
methods. For this, we differentiate between the various classes of dialogue
systems (task-oriented dialogue systems, conversational dialogue systems, and
question-answering dialogue systems). We cover each class by introducing the
main technologies developed for the dialogue systems and then by presenting the
evaluation methods regarding this class
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