85,300 research outputs found
A Survey of Monte Carlo Tree Search Methods
Monte Carlo tree search (MCTS) is a recently proposed search method that combines the precision of tree search with the generality of random sampling. It has received considerable interest due to its spectacular success in the difficult problem of computer Go, but has also proved beneficial in a range of other domains. This paper is a survey of the literature to date, intended to provide a snapshot of the state of the art after the first five years of MCTS research. We outline the core algorithm's derivation, impart some structure on the many variations and enhancements that have been proposed, and summarize the results from the key game and nongame domains to which MCTS methods have been applied. A number of open research questions indicate that the field is ripe for future work
Putting theory oriented evaluation into practice
Evaluations of gaming simulations and business games as teaching devices are typically end-state driven. This emphasis fails to detect how the simulation being evaluated does or does not bring about its desired consequences. This paper advances the use of a logic model approach which possesses a holistic perspective that aims at including all elements associated with the situation created by a game. The use of the logic model approach is illustrated as applied to Simgame, a board game created for secondary school level business education in six European Union countries
Aligning operational and corporate goals: a case study in cultivating a whole-of-business approach using a supply chain simulation game
This paper outlines the development and use of an interactive computer-based supply chain game to facilitate the alignment of disconnected operational and corporate goals. A multi-enterprise internal cattle supply chain was simulated targeting the operational property managers and the overall impacts of their decision making on corporate goals A three stage multidisciplinary approach was used. A case study based financial analysis was undertaken across the internal cattle supply chain, a participative action research component (developing the game to simulate the flow of product and associated decisions and financial
transactions through the internal supply chain of the company for different operational scenarios using measurable and familiar operational and financial criteria as tracking tools), and a qualitative analysis of organisational learning through player debriefing following
playing the game. Evaluation of the managers' learning around the need for a change in general practice to address goal incongruence was positive evidenced by changes in practice and the game regarded by the users as a useful form of organisational training. The game provided property managers with practical insights into the strategic implications of their enterprise level decisions on the internal supply chain and on overall corporate performance.
The game is unique and is a tool that can be used to help address an endemic problem across multi-enterprise industries in the agrifood sector in Australia
Helping AI to Play Hearthstone: AAIA'17 Data Mining Challenge
This paper summarizes the AAIA'17 Data Mining Challenge: Helping AI to Play
Hearthstone which was held between March 23, and May 15, 2017 at the Knowledge
Pit platform. We briefly describe the scope and background of this competition
in the context of a more general project related to the development of an AI
engine for video games, called Grail. We also discuss the outcomes of this
challenge and demonstrate how predictive models for the assessment of player's
winning chances can be utilized in a construction of an intelligent agent for
playing Hearthstone. Finally, we show a few selected machine learning
approaches for modeling state and action values in Hearthstone. We provide
evaluation for a few promising solutions that may be used to create more
advanced types of agents, especially in conjunction with Monte Carlo Tree
Search algorithms.Comment: Federated Conference on Computer Science and Information Systems,
Prague (FedCSIS-2017) (Prague, Czech Republic
+SPACES: Serious Games for Role-Playing Government Policies
The paper explores how role-play simulations can be used to support policy discussion and refinement in virtual worlds. Although the work described is set primarily within the context of policy formulation for government, the lessons learnt are applicable to online learning and collaboration within virtual environments. The paper describes how the +Spaces project is using both 2D and 3D virtual spaces to
engage with citizens to explore issues relevant to new government policies. It also focuses on the most challenging part of the project, which is to provide environments that can simulate some of the complexities of real life. Some examples of different approaches to simulation in virtual spaces are provided and the issues associated with them are further examined.
We conclude that the use of role-play simulations seem to offer the most benefits in terms of providing a generalizable framework for citizens to engage with real issues arising from future policy decisions. Role-plays have also been shown to be a useful tool for engaging learners in the complexities of real-world issues, often generating insights which would not be possible using more conventional techniques
Using virtual worlds for online role-play
The paper explores the use of virtual worlds to support online role-play as a collaborative activity. This paper describes some of the challenges involved in building online role-play environments in a virtual world and presents some of the ideas being explored by the project in the role-play applications being developed. Finally we explore how this can be used within the context of immersive education and 3D collaborative environments
Traditional Wisdom and Monte Carlo Tree Search Face-to-Face in the Card Game Scopone
We present the design of a competitive artificial intelligence for Scopone, a
popular Italian card game. We compare rule-based players using the most
established strategies (one for beginners and two for advanced players) against
players using Monte Carlo Tree Search (MCTS) and Information Set Monte Carlo
Tree Search (ISMCTS) with different reward functions and simulation strategies.
MCTS requires complete information about the game state and thus implements a
cheating player while ISMCTS can deal with incomplete information and thus
implements a fair player. Our results show that, as expected, the cheating MCTS
outperforms all the other strategies; ISMCTS is stronger than all the
rule-based players implementing well-known and most advanced strategies and it
also turns out to be a challenging opponent for human players.Comment: Preprint. Accepted for publication in the IEEE Transaction on Game
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