Better AI in Unity - GOAP (Goal Oriented Action Planning)
TLDRThe video script delves into the intricacies of creating a Goal-Oriented Action Planning (GOAP) system for game AI, emphasizing its superiority over traditional methods in enhancing believability and complexity. It outlines the development of GOAP by Jeff Orin, its flexible approach to real-world decision-making, and its successful implementation in games. The script provides a step-by-step guide on building a GOAP system from scratch, covering the definition of beliefs, actions, and goals, and the creation of a planning algorithm. The implementation is demonstrated through a practical example, showcasing the system's ability to adapt and respond to changing game environments. The video concludes with suggestions for potential improvements and extensions, highlighting the potential of GOAP in creating sophisticated and dynamic game AI.
Takeaways
- 🎯 Goal-Oriented Action Planning (GOAP) is a technique used to create adaptive and dynamic AI behaviors in games.
- 🤖 GOAP was developed by Jeff Orin at MIT in the early 2000s and has been influential in advancing AI planning techniques.
- 🔄 Unlike traditional methods, GOAP decouples actions and goals, allowing for more flexibility and unpredictability in AI behavior.
- 📋 In GOAP, actions are defined by their preconditions and effects, which the planning system uses to form coherent plans.
- 🛠️ Implementing GOAP in game AI can significantly reduce the overhead of extending and enhancing AI capabilities, as adding new actions, goals, and beliefs becomes easier.
- 💡 The planning component of GOAP operates in a goal-driven manner, working backwards from the desired goal to the current state to ensure efficient paths are chosen.
- 🏗️ Building a GOAP system involves defining beliefs, actions, and goals, and then creating a planner that stitches these elements together to output a plan of action.
- 🔍 Sensors provide the AI agent with information about its environment, which is used in conjunction with beliefs to inform decision-making.
- 📈 The planning process can be visualized as a graph with nodes representing actions and their associated beliefs, from which the planner finds a path to achieve the goal.
- 📝 A node-based editor or third-party tools like Node Canvas could be used to simplify the creation and management of strategies, beliefs, actions, and goals in GOAP.
Q & A
What is GOP and how does it improve AI planning in games?
-GOP (Goal-Oriented Action Planning) is a planning technique that allows for adaptive and dynamic decision-making in AI agents. It improves AI planning by decoupling actions and goals, enabling agents to analyze their environment, consider various actions, and devise a plan that best achieves their goals given the current situation. This leads to more believable and less predictable AI behavior, enhancing the player's gaming experience.
How is GOP different from traditional AI planning methods?
-Unlike traditional methods where actions and goals are rigidly tied together, GOP allows for a more flexible approach. In GOP, actions and goals are not strictly bound, which leads to a more adaptive and dynamic planning process. This enables AI agents to explore different strategies and come up with multiple, often unexpected solutions based on their current state and the environment.
What is the significance of the planning component in GOP?
-The planning component in GOP is crucial as it operates in a goal-driven manner, working backwards from the desired goal to the current state. This reverse engineering of plans ensures that every step taken is a step towards achieving the goal, with the planner evaluating multiple paths and choosing the most efficient one. It's this component that makes GOP particularly effective in creating AI that can adapt to changing circumstances and find optimal solutions.
How does implementing GOP in game AI affect the development process?
-Implementing GOP in game AI significantly reduces the overhead of extending and enhancing AI capabilities. It makes adding new actions, goals, and beliefs very easy, allowing developers to continually evolve and deepen the game's AI complexity with minimal effort. This leads to more sophisticated and engaging gameplay experiences for players.
What are the preconditions and effects of an action in GOP?
-In GOP, each action is defined by its preconditions and effects. Preconditions are the conditions that must be true for an action to be executed, while effects are the outcomes of executing the action. The planning system uses these definitions to stitch actions together, forming a coherent plan that transitions the agent from its current state to the desired goal state.
How does the agent's belief system function within GOP?
-The agent's belief system in GOP is foundational to its decision-making process. Beliefs can evaluate as true or false and represent the agent's knowledge about the game world, such as the player's location or available resources. The agent uses these beliefs to analyze its environment and make informed decisions about which actions to take in order to achieve its goals.
What is the role of sensors in GOP and how do they contribute to the agent's beliefs?
-Sensors in GOP provide the agent with information about its environment. They detect events and changes happening around the agent, such as the player's proximity or the spawning of health packs. This information is used to update the agent's beliefs, allowing it to reason about the world and make decisions based on current conditions.
How does the GOP system handle multiple goals and prioritize them?
-In the GOP system, goals are assigned a priority level to determine their importance compared to other goals. The system can handle multiple goals by ordering them based on priority and attempting to achieve the highest priority goal that has not yet been fulfilled. If a goal's desired effects are already true or if the goal is deemed less important than the current goal, the system may choose to pursue a different goal instead.
What is the significance of the depth-first search (DFS) used in the GOP planning algorithm?
-The depth-first search (DFS) used in the GOP planning algorithm is a method for traversing the graph of possible actions and outcomes to find a path to the goal state. It starts from the goal and works backwards, exploring each branch of possible actions until it finds a complete path that satisfies all the preconditions. If a path is found, the algorithm can construct a plan by following the actions in reverse order, from the last action taken to the first.
How can the GOP system be extended or improved in future developments?
-The GOP system can be extended or improved by integrating it with node-based editors for strategies, beliefs, actions, and goals, making it easier to visualize and modify the AI's decision-making process. Additionally, using dependency injection or a service locator can simplify testing and make the system more modular. Finally, exploring more sophisticated pathfinding algorithms like A* or Dijkstra's algorithm can further optimize the planning process.
Outlines
🤖 Understanding GOP in AI Game Design
This paragraph introduces the concept of Goal Oriented Action Planning (GOP) in game design, emphasizing its significance in creating believable enemy AI. It explains that GOP allows for adaptive and dynamic planning processes, enabling AI agents to analyze their environment and devise plans to achieve their goals. The paragraph also mentions the development of GOP by Jeff Orin at MIT and its successful implementation in the game 'Fear', which set a new standard for NPC behavior.
🔍 Decoupling Actions and Goals in GOP
The paragraph delves into the mechanics of GOP, highlighting how actions and goals are decoupled in this system. It contrasts GOP with finite state machines and explains how GOP allows for multiple strategies and solutions based on the agent's current state and environment. The paragraph also outlines how actions are defined by their preconditions and effects, and how the planning component operates in a goal-driven manner, working backwards from the desired goal to the current state.
🛠️ Implementing GOP in Game AI
This section discusses the implementation of GOP in game AI, explaining how it can enhance the realism and unpredictability of NPC behaviors while reducing the overhead of extending and enhancing AI capabilities. It outlines the overall architecture of a GOP agent, including the collections of beliefs, actions, and goals, and the process of feeding these into the planner to output a plan. The paragraph also touches on the importance of understanding the planning agent's reasoning process.
📚 Defining Agent Beliefs in GOP
The paragraph focuses on the foundational role of agent beliefs in GOP. It describes the process of defining beliefs, including their evaluation as true or false, and the creation of a belief factory for easier belief generation. The section also discusses the implementation of sensors that provide information about the game environment, such as player proximity or health pack spawns, and how these sensors contribute to the agent's beliefs.
🎯 Setting Up Actions and Goals in GOP
This part of the script details the process of setting up actions and goals within the GOP framework. Actions are described as having a cost, preconditions, and effects, and the paragraph outlines the creation of a private Constructor and a builder for actions. Goals, on the other hand, are defined by their priority level and the set of beliefs that represent desired outcomes. The section also discusses the creation of strategies for actions, such as idle and wander strategies, and the importance of these strategies in achieving goals.
🤹♂️ Agent's Decision-Making Process in GOP
The paragraph explains the decision-making process of an agent using GOP, including the initialization of sensors, beliefs, actions, and goals. It describes the setup of timers for stat degradation and the agent's need to reevaluate goals based on changing conditions. The paragraph also covers the agent's need to track its current goal and action plan, as well as the storage and management of beliefs, actions, and goals within the agent's system.
🔄 Developing a GOP Planner
This section delves into the development of a GOP planner, which is responsible for creating a plan of action based on the agent's goals. It outlines the process of ordering goals by priority, filtering out already achieved goals, and using depth-first search to explore potential solutions. The paragraph also discusses the creation of a graph of nodes representing possible actions and their outcomes, and the algorithm for finding a complete path that satisfies all preconditions.
🎮 Testing the GOP System in a Game Environment
The paragraph describes the practical application and testing of the GOP system within a game environment. It covers the setup of the agent with references to various components, the implementation of strategies for maintaining health and stamina, and the agent's response to changing conditions such as low health or player proximity. The section also discusses the agent's goal prioritization and the execution of plans, including the interruption of current actions for higher priority goals.
💡 Enhancing and Extending the GOP Framework
The final paragraph discusses potential enhancements and extensions to the GOP framework. It suggests the development of a node-based editor for strategies, beliefs, actions, and goals, as well as the use of dependency injection or a service locator for easier testing and isolation of components. The paragraph also mentions the possibility of using more sophisticated pathfinding algorithms and the integration of third-party tools for improved functionality.
Mindmap
Keywords
💡Goal-Oriented Action Planning (GOAP)
💡AI Planning
💡NPC Behavior
💡Decoupling Actions and Goals
💡Preconditions and Effects
💡Planning Component
💡Sensors
💡Beliefs
💡Strategies
💡Agent
💡Planning Agent
Highlights
GOP (Goal Oriented Action Planning) is a technique that enhances AI planning by allowing for adaptive and dynamic decision-making processes.
GOP was developed by Jeff Orin at MIT in the early 2000s, marking a significant leap in AI planning techniques.
In GOP, actions and goals are decoupled, which means agents can analyze their environment and devise plans that best achieve their goals given the current situation.
GOP enables agents to explore different strategies, such as finding food, trading with others, or stealing, each with its own set of preconditions, effects, and associated costs.
The planning component in GOP operates in a goal-driven manner, working backwards from the desired goal to the current state, ensuring that every step taken is towards achieving the goal.
Implementing GOP in game AI elevates realism and unpredictability of NPC behaviors and significantly reduces the overhead in extending and enhancing AI capabilities.
GOP agents use sensors to understand their environment, and these sensors can detect events such as a player coming near or a health pack spawning.
Beliefs in the GOP system can evaluate as true or false, and they are foundational to the agent's decision-making process.
Actions in GOP are defined by their preconditions and effects, and the planning system stitches these actions together to form a coherent plan.
Goals in GOP are prioritized, and the system focuses on achieving the most important goals first, ensuring that the agent's actions align with its highest priorities.
The GOP planning process involves creating a graph of nodes, where each node represents a potential action or state in the planning process.
Depth-first search (DFS) is used in the GOP planning algorithm to explore possible solutions and find the most efficient path to achieving the goal.
The GOP system allows for easy addition of new actions, goals, and beliefs, making it a flexible and scalable solution for complex AI behavior in games.
A node-based editor or third-party tools like Node Canvas can be used to visually design and test GOP systems, making the process more accessible and efficient.
Dependency injection or a service locator can be implemented in the GOP agent for easier testing and management of its components.
Advanced planning algorithms like A* or Dijkstra's algorithm can be integrated into the GOP system for more sophisticated pathfinding and decision-making.
