Set Goals, Constraints, and Success Criteria
Last updated
Last updated
The performance goal is the most important KPI or metric you will use to evaluate your agent system's success. This goal directs your agent as it trains. The AI learning technology within the agent will reward the agent system when it gets closer to the goal, helping it to improve.
For many business use cases, the top-level goal will be expressed in terms of profit or ROI. There are many factors to consider in the production scheduling use case, but profit is the ultimate goal, so it is the most important KPI.
Some use cases will have multiple goals the agent system must meet simultaneously. In fact, the need to balance more than one goal is a sign that a use case is a good fit for Machine Teaching. In , the agent system needs to get the right chemical product concentration and keep the tank's temperature from becoming dangerously hot.
When you in the UI, you can use natural language to describe your goals for the use case. Composabl's copilot assistant uses this information to create a starter goal in the format the platform can use for training. This goal may be ready to use, but you will likely need to adjust it.
To edit a goal created by the copilot or to create a new goal, follow these steps:
Navigate to the use case page
Click Set up goal and enter a name and description for your goal.
Click Add condition to define the goal.
Use the dropdown menus to select the variables and then define the parameters for each goal, constraint, or success criterion you want to include.
Goals define what a skill agent system should do. Goals apply to one of the sensor variables and are defined using one of three possible directives:
Maximize: Maximize the value of the variable
Minimize: Minimize the value of the variable
Maintain: Keep the variable at a specified value
Constraints set the boundaries for the skill agent. They define rules that the agent system must follow during operation. Constraints are defined using one of two possible directives:
Avoid: The skill agent learns to keep the variable from reaching a specified value or range through withholding rewards
Terminate: When the actions of the skill agent lead to certain conditions within a variable, the skill agent has failed and must stop and start a new episode
Success criteria tell the skill agent when it's doing something right. They are defined using one of two possible directives:
Approach: The skill agent learns to get close to a specified value by getting an increased reward
Succeed: When the success criteria are achieved, the session ends, and a new one begins so that the skill agent can keep practicing and learn to win every time
Goals, constraints, and success criteria transform complex AI agent building into an intuitive process that eliminates the need for programming expertise.
You directly apply your domain knowledge without coding by simply defining what you want the agent to achieve and what conditions to respect. This approach reduces development time while ensuring AI agents embody real-world operational wisdom, bridging the gap between technical AI capabilities and practical industrial knowledge in complex physical environments.
For example, for , we want to maximize the concentration of the product, Ca.
For example, we want to avoid the temperature, T, getting above 400 degrees Kelvin in use case.