199,598 research outputs found
What Drives People's Choices in Turn-Taking Games, if not Game-Theoretic Rationality?
In an earlier experiment, participants played a perfect information game
against a computer, which was programmed to deviate often from its backward
induction strategy right at the beginning of the game. Participants knew that
in each game, the computer was nevertheless optimizing against some belief
about the participant's future strategy. In the aggregate, it appeared that
participants applied forward induction. However, cardinal effects seemed to
play a role as well: a number of participants might have been trying to
maximize expected utility.
In order to find out how people really reason in such a game, we designed
centipede-like turn-taking games with new payoff structures in order to make
such cardinal effects less likely. We ran a new experiment with 50
participants, based on marble drop visualizations of these revised payoff
structures. After participants played 48 test games, we asked a number of
questions to gauge the participants' reasoning about their own and the
opponent's strategy at all decision nodes of a sample game. We also checked how
the verbalized strategies fit to the actual choices they made at all their
decision points in the 48 test games.
Even though in the aggregate, participants in the new experiment still tend
to slightly favor the forward induction choice at their first decision node,
their verbalized strategies most often depend on their own attitudes towards
risk and those they assign to the computer opponent, sometimes in addition to
considerations about cooperativeness and competitiveness.Comment: In Proceedings TARK 2017, arXiv:1707.0825
Gaming techniques and the product development process : commonalities and cross-applications
The use of computer-based tools is now firmly embedded within the product development process, providing a wide range of uses from visualisation to analysis. However, the specialisation required to make effective use of these tools has led to the compartmentalisation of expertise in design teams, resulting in communication problems between individual members. This paper therefore considers how computer gaming techniques and strategies could be used to enhance communication and group design activities throughout the product design process
Developing Artificial Intelligence Agents for a Turn-Based Imperfect Information Game
Artificial intelligence (AI) is often employed to play games, whether to entertain human opponents, devise and test strategies, or obtain other analytical data. Games with hidden information require specific approaches by the player. As a result, the AI must be equipped with methods of operating without certain important pieces of information while being aware of the resulting potential dangers. The computer game GNaT was designed as a testbed for AI strategies dealing specifically with imperfect information. Its development and functionality are described, and the results of testing several strategies through AI agents are discussed
Human-Agent Decision-making: Combining Theory and Practice
Extensive work has been conducted both in game theory and logic to model
strategic interaction. An important question is whether we can use these
theories to design agents for interacting with people? On the one hand, they
provide a formal design specification for agent strategies. On the other hand,
people do not necessarily adhere to playing in accordance with these
strategies, and their behavior is affected by a multitude of social and
psychological factors. In this paper we will consider the question of whether
strategies implied by theories of strategic behavior can be used by automated
agents that interact proficiently with people. We will focus on automated
agents that we built that need to interact with people in two negotiation
settings: bargaining and deliberation. For bargaining we will study game-theory
based equilibrium agents and for argumentation we will discuss logic-based
argumentation theory. We will also consider security games and persuasion games
and will discuss the benefits of using equilibrium based agents.Comment: In Proceedings TARK 2015, arXiv:1606.0729
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
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
Unmasking Clever Hans Predictors and Assessing What Machines Really Learn
Current learning machines have successfully solved hard application problems,
reaching high accuracy and displaying seemingly "intelligent" behavior. Here we
apply recent techniques for explaining decisions of state-of-the-art learning
machines and analyze various tasks from computer vision and arcade games. This
showcases a spectrum of problem-solving behaviors ranging from naive and
short-sighted, to well-informed and strategic. We observe that standard
performance evaluation metrics can be oblivious to distinguishing these diverse
problem solving behaviors. Furthermore, we propose our semi-automated Spectral
Relevance Analysis that provides a practically effective way of characterizing
and validating the behavior of nonlinear learning machines. This helps to
assess whether a learned model indeed delivers reliably for the problem that it
was conceived for. Furthermore, our work intends to add a voice of caution to
the ongoing excitement about machine intelligence and pledges to evaluate and
judge some of these recent successes in a more nuanced manner.Comment: Accepted for publication in Nature Communication
- …