On the Design of an Artificial Player for a Popular Word Game

This paper describes the design of an implemented software system intended to accomplish a specific natural language processing task. The targeted task is a challenge of the Evaluation Campaign of Natural Language Processing and Speech Tools for Italian proposed in 2020. The challenge is to design and implement an artificial player for the closing game of a popular Italian television show. Given five words, the goal of the player is to find a word related to, but also different from, the given words. The design of the proposed artificial player is discussed by presenting the dataset used to acquire sufficient linguistic knowledge and by briefly describing the algorithm used to play the game. A preliminary experimental evaluation of the artificial player is also discussed

Membuat Aplikasi Game Othello Dengan Menggunakan Algoritma Greedy

Game is an application or software made for entertainment, education and a combination of both. Game itself is using artificial intelligence (Artificial Intelligent / AI), some are not using artificial intelligence (Artificial Intelligent / AI). One game that uses artificial intelligence is Othello game. Othello game application is made by using a greedy algorithm with three difficulty levels, namely : easy, normal and hard. The characteristics of Othello game is the shaped of the game a square with a size of 8 x 8 and has a coin-shaped pieces in black and white that represent each player. The technique of this game the player must be able to block the opponent's coins as possible and seek measures to win the game. Game application built using waterfall method and modeling language Unified Modeling Language (UML), and tested by the method of black box testing and white box testing. Game application can to be used as a means of entertainment and thinking skills of players

Preliminary Experiments on an Improved Artificial Player for a Word Association Game

This paper presents recent developments of a software system that acts as an artificial player for a popular word association game. The game was proposed for the Evaluation Campaign of Natural Language Processing and Speech Tools for Italian in 2020, and it attracted the interest of various researchers. Several aspects of the recent developments of the artificial player are discussed, from the collection of the texts used to acquire sufficient linguistic knowledge, to the improvements of the algorithm employed to play the game. Preliminary, but encouraging, experimental results are also discussed in comparison with other artificial players for the same game

Ghigliottin-AI @ EVALITA2020: Evaluating artificial players for the language game “La Ghigliottina”

Evaluating Artificial Players for the Language Game “La Ghigliottina” (Ghigliottin-AI) task is one of the tasks organized in the context of the 2020 EVALITA edition, a periodic evaluation campaign of Natural Language Processing (NLP) and speech tools for the Italian language. Ghigliottin-AI participants are asked to build an artificial player able to solve “La Ghigliottina”, namely the final game of an Italian TV show called “L'Eredità”. The game involves a single player who is given a set of five words unrelated to each other, but related with a sixth word that represents the solution to the game. Fourteen teams registered to Ghigliottin-AI. Nevertheless, only two teams submitted their run. In order to evaluate the submitted systems, we rely on an API base methodology, via a Remote Evaluation Server (RES). In this report we describe the Ghigliottin-AI task, the data, the evaluation and we discuss results

Preliminary Experiments on an Improved Artificial Player for a Word Association Game

This paper presents recent developments of a software system that acts as an artificial player for a popular word association game. The game was proposed for the Evaluation Campaign of Natural Language Processing and Speech Tools for Italian in 2020, and it attracted the interest of various researchers. Several aspects of the recent developments of the artificial player are discussed, from the collection of the texts used to acquire sufficient linguistic knowledge, to the improvements of the algorithm employed to play the game. Preliminary, but encouraging, experimental results are also discussed in comparison with other artificial players for the same game

Automated Theorem Proving for General Game Playing

While automated game playing systems like Deep Blue perform excellent within their domain, handling a different game or even a slight change of rules is impossible without intervention of the programmer. Considered a great challenge for Artificial Intelligence, General Game Playing is concerned with the development of techniques that enable computer programs to play arbitrary, possibly unknown n-player games given nothing but the game rules in a tailor-made description language. A key to success in this endeavour is the ability to reliably extract hidden game-specific features from a given game description automatically. An informed general game player can efficiently play a game by exploiting structural game properties to choose the currently most appropriate algorithm, to construct a suited heuristic, or to apply techniques that reduce the search space. In addition, an automated method for property extraction can provide valuable assistance for the discovery of specification bugs during game design by providing information about the mechanics of the currently specified game description. The recent extension of the description language to games with incomplete information and elements of chance further induces the need for the detection of game properties involving player knowledge in several stages of the game. In this thesis, we develop a formal proof method for the automatic acquisition of rich game-specific invariance properties. To this end, we first introduce a simple yet expressive property description language to address knowledge-free game properties which may involve arbitrary finite sequences of successive game states. We specify a semantic based on state transition systems over the Game Description Language, and develop a provably correct formal theory which allows to show the validity of game properties with respect to their semantic across all reachable game states. Our proof theory does not require to visit every single reachable state. Instead, it applies an induction principle on the game rules based on the generation of answer set programs, allowing to apply any off-the-shelf answer set solver to practically verify invariance properties even in complex games whose state space cannot totally be explored. To account for the recent extension of the description language to games with incomplete information and elements of chance, we correctly extend our induction method to properties involving player knowledge. With an extensive evaluation we show its practical applicability even in complex games

Ghigliottin-AI @ EVALITA2020: Evaluating Artificial Players for the Language Game “La Ghigliottina”

Evaluating Artificial Players for the Language Game “La Ghigliottina” (Ghigliottin-AI) task is one of the tasks organized in the context of the 2020 EVALITA edition, a periodic evaluation campaign of Natural Language Processing (NLP) and speech tools for the Italian language. Ghigliottin-AI participants are asked to build an artificial player able to solve “La Ghigliottina”, namely the final game of an Italian TV show called “L’Eredità”. The game involves a single player who is given a set of five words unrelated to each other, but related with a sixth word that represents the solution to the game. Fourteen teams registered to Ghigliottin-AI. Nevertheless, only two teams submitted their run. In order to evaluate the submitted systems, we rely on an API base methodology, via a Remote Evaluation Server (RES). In this report we describe the Ghigliottin-AI task, the data, the evaluation and we discuss results

Permainan Strategi Battleship Menggunakan Backtracking Dan Depth First Search

Battleship is a board game. It can be play by shooting empty boxes on the game board until player or computer (AI) win. The purpose of this research is to create a computer-based battleship game that has Artificial Intelligence (AI) so this game does not require two people to play. Besides not requiring two people to play, players can also enjoy this battleship game with artificial intelligence (AI) which is equipped with algorithms. The algorithm to make this battleship game is by using two algorithm backtracking and DFS. This application is to allow players run this application with an algorithm, that is equipped with a search for the player ship and the player can play the game without placing their ship first.  The description of this battleship game is like a destructive game, but the theme taken in making this application is whether the search for a the player ship using the backtracking and DFS algorithm is a incredibly efficient algorithm in this battleship game application. This battleship game application is made by using the waterfall method with Unified Modeling Language (UML) and python programming. The results of this research is a battleship games that have artificial intelligence (AI) that can be adapted to the player's ability, so that it can be used as an entertainment. Other than as an entertainment this application should be developed with more attractive features