Penerapan Metode Goal Oriented Action Planning untuk Agent AI pada Turn Based Tactics Video Game - Ryan Chandra Kusuma; Liliana Liliana; Hans Juwiantho (Jurnal Infra, 2021)
In turn-based tactics game, difficulties often placed on resources owned by enemies. Players have to do repetitive action to counterbalance enemy’s resources. To make players spent more time on strategies rather than counterbalance enemy’s resources, goal-oriented action planning will be implemented for AI. It’s expected that AI GOAP even without extra resources can replace AI FSM with extra resources.Goal-Oriented Action Planning (GOAP) is a decision-making method that capable of making a character not only do what it will do, but also determine how to do it. A* is a method that looks for a path by exploring the minimum number of nodes with minimum cost solution. This research combines GOAP and A* search. GOAP in this research has several variations of actions based on health points. Result of the research shows that AI GOAP without extra resources has 33.33% winrate against AI FSM with extra resources, and 86.66% against AI FSM with extra resources but reduced power unit. The results of respondents from various players with different experiences show that the difficulty of AI FSM with extra resources is higher, the level of player satisfaction and AI’s realistic level is higher when fought against AI GOAP without extra resources.