Descripción:

The cooperative card game Hanabi has become of increasing interest in the community, since it combines partially hidden information with information exchange using restricted communication channels. In this paper, we describe AI agents that are designed to play the game with human players. Our agents make use of the fact that human players expect other players to act intentionally by formulating goals of their own and planning how to achieve them. They then use the available actions available to communicate their plan to the human player. On the flip side, our agents also interpret the actions performed by the human player as containing information about their plans. We present two different variants of our agent that perform this interpretation in different ways. Additionally, since part of human communication happens in subtle, indirect ways, we also demonstrate that our agent can use the timing of the human player's actions as additional information. In order to validate our agents, we have performed two separate experiments. One was done to validate the intentional component of the agents, while the other focused on the interpretation of received information. In this article, we also present the results obtained from these two experiments.

Tipo de publicación: Journal Article

Publicado en: IEEE Transactions on Games

Descripción:

Social deduction games are a genre of board games in which a group of players is secretly assigned roles and each player tries to determine the other players’ roles. However, some roles have an incentive to not be found, and the games typically allow players to lie freely. Playing such games is a challenging task for AI agents, because they need to not only determine the probability that each statement made by the other players is truthful, but also come up with convincing lies themselves. In this paper, we present AI agents designed to play one particular such game, One Night Ultimate Werewolf, with human players. We discuss the different deliberation strategies our agents use to determine what they should say, and when they should change their plan. To determine how these different deliberation strategies are perceived by human players, we performed an experiment in which participants played a Unity implementation of the game with each of the three deliberation strategies. We present the results of this experiment, which show that commitment to plans has a measurable effect on player perception and provide a trade-off between consistency and potential for high performance of the agent.

Tipo de publicación: Conference Proceedings

Publicado en: Conference on Artificial Intelligence and Interactive Digital Entertainment

Descripción:

Cooperation between human players and AI agents in games is a subject of great interest in the research community. In this paper we propose a new domain as a challenge for future AI research: the cooperative game Pandemic. This game represents a challenge because it requires cooperation between players in an environment with incomplete information. Additionally, we propose a first approach for a cooperative agent for the game, that uses planning in conjunction with plan recognition. We also propose an experiment to test the mentioned approach. In this paper we argue why Pandemic makes a compelling domain for AI research, the status of our project, as well as which challenges remain to be addressed.

Tipo de publicación: Conference Paper

Publicado en: Sixteenth Artificial Intelligence and Interactive Digital Entertainment Conference

Descripción:

One of the biggest challenges for farmers is the prevention of disease appearance on crops. Governments around the world control border product entry to reduce the number of foreign diseases affecting local producers. Evenmore, it is also important to reduce the spread of crop diseases as quickly as possible and in early stages of propagation, to enable farmers to attack them on time, or to remove the affected plants. In this research, we propose the use of convolutional neural networks to detect diseases in horticultural crops. We compare the results of disease classification in images of plant leaves, in terms of performance, time execution, and classifier size. In the analysis, we implement two distinct classifiers, a densenet-161 pre-trained model and a custom created model. We concluded that for disease detection in tomato crops, our custom model has better execution time and size, and the classification performance is acceptable. Therefore, the custom model could be useful to use to create a solution that helps small farmers in rural areas in resource-limited mobile devices.

Tipo de publicación: Book Chapter

Publicado en: Advances in Sustainability Science and Technology