A Neural-Symbolic Cognitive Architecture Framework for Handling Novel Entities in Open Worlds

Background

Traditional AI research assumes that intelligent agents operate in a “closed world”, where all task-relevant concepts in the environment are known, and no new unknown situations will arise. However, in the open real world, novel entities that violate the agent’s prior knowledge are bound to appear. This paper proposes a novel hybrid neural-symbolic reasoning architecture that enables agents to detect and adapt to novel entities, thereby allowing them to complete tasks in open worlds.

Definition of Novel Entities

The paper views novel entities as agent-relevant concepts. If an agent cannot derive the representation of an entity from its knowledge base, then that entity is considered novel to the agent. Based on the degree to which novel entities affect the agent’s task completion, the paper categorizes novel entities into the following types:

• Prohibitive Novel Entities: The agent must represent and reason about these novel entities in order to generate plans that can complete the task.
• Blocking Novel Entities: These entities cause the agent’s actuators to fail during execution.
• Beneficial Novel Entities: Mastering these novel entities can help the agent complete the task more effectively.
• Irrelevant Novel Entities: These have no impact on task completion but may incur additional costs.

Neural-Symbolic Cognitive Architecture

The paper proposes a hybrid neural-symbolic architecture for detecting and adapting to novel entities. The architecture includes the following main components:

Symbolic Reasoning

• Knowledge Base: Stores symbolic state descriptions, rules, and operators.
• Task Planner: Generates action plans based on the goal state.
• Goal Manager: Monitors the discrepancy between the current state and the expected state, detecting prohibitive and blocking novel entities.

Neural Reasoning

• Vision Model: Uses deep autoencoders to detect visual novelties.
• Agent Model: Models other agents’ behaviors using behavior cloning to detect behavioral anomalies.

Novelty Exploration

• Contains symbolic exploration algorithms and reinforcement learning explorers.
• Upon detecting novel entities, generates appropriate exploration strategies to learn how to adapt to the novelties.

Evaluation

The paper comprehensively evaluates the individual components and the overall system of the architecture in the Polycraft sandbox environment. Various novelty scenarios are set up, where the agent needs to detect and adapt to novel entities to complete the task of crafting a “pogo stick”. The evaluation metrics include:

• Novel Entity Detection Performance: False positive rate, true positive rate, etc.
• Task Completion Performance: The agent’s ability to complete the task after introducing novel entities.

The evaluation results show that the proposed architecture can efficiently detect and adapt to most novel entities, maintaining relatively good task completion performance even after introducing novel entities.

Summary

This paper proposes a hybrid neural-symbolic cognitive architecture that endows agents with the ability to detect and adapt to novel entities in open worlds. The architecture combines techniques such as symbolic planning, counterfactual reasoning, reinforcement learning, and deep computer vision to effectively explore unknown situations and update the knowledge base. Evaluations in the Polycraft environment demonstrate the outstanding performance of the proposed architecture in handling novel entities. This work provides a valuable solution for the development of open-world AI systems.