To develop planning techniques that can effectively reason about and prune irrelevant objects in large, complex environments like Minecraft, we can consider the following strategies:
Task Scoping Algorithms: Implement algorithms like task scoping, as introduced in previous research, to automatically generate task-specific abstractions for planning in open-scope models. These algorithms can help identify and remove irrelevant objects and operators from the planning domain, improving planner performance by focusing on the essential elements of the task.
Lifted Planning: Explore lifted planning approaches that operate directly on the lifted representation of the problem. Lifted planning techniques can handle symbolic representations more efficiently, potentially reducing the need for extensive grounding and enabling planners to reason about high-level concepts without the need to consider every individual object.
Predicate Invention: Investigate techniques like predicate invention, where planners can dynamically generate new predicates during planning to capture relevant information and abstract away unnecessary details. This adaptive approach can help planners adapt to the specific requirements of complex tasks and environments like those in Minecraft.
Integration of Learning: Integrate machine learning and data-driven approaches to assist in identifying relevant objects and relationships in the environment. By leveraging learned representations and patterns, planners can focus on the most critical aspects of the task and avoid getting bogged down by irrelevant details.
Hierarchical Planning: Explore hierarchical planning methods that can break down complex tasks into subtasks and levels of abstraction. By organizing the planning process hierarchically, planners can effectively manage the complexity of large environments and focus on relevant objects and actions at each level of the hierarchy.