Latent Graph Exploration Using CSCG for Faster Cognitive Mapping
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing techniques for generating a cognitive map of an environment require extensive interaction and significant computational resources, leading to inefficiency and potential damage to real-world agents, especially in complex environments with challenging topologies.
Innovation Solution
The use of a latent graph model, specifically a Clone Structured Cognitive Graph (CSCG), which accounts for aliased observations and employs a closed-form utility function to select actions that maximize information gain, reducing the need for extensive interaction and resource consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional techniques use sequences of random actions for environment exploration, then the agent can interact with the environment, but the process requires extensive interaction and significant computational resources
Solution Approach 1:
The system performs preliminary actions by maintaining a utility measure for each state-action pair and using closed-form utility functions to pre-evaluate potential actions. This allows the agent to plan ahead and select actions that maximize information gain without requiring extensive random exploration, thereby reducing both time and computational resources needed for cognitive map generation.
Solution Approach 2:
The invention changes the parameter of action selection from random to utility-based selection. By computing utility measures using closed-form functions and selecting actions that satisfy information gain thresholds, the system transforms the exploration process from inefficient random sampling to targeted, optimized action selection, improving productivity while reducing energy consumption.
2Measurement precision
If the agent performs extensive random actions to explore the environment, then the cognitive map can be generated, but the process takes significant time and computational resources
Solution Approach 1:
The utility measure serves as an intermediary that bridges the gap between random exploration and accurate cognitive map generation. By introducing this intermediate evaluation layer that computes information gain for potential actions, the system can achieve measurement precision (accurate cognitive map) without the time loss associated with extensive random exploration.
Solution Approach 2:
The invention substitutes the mechanical system of random trial-and-error exploration with a computational system based on closed-form utility functions. This replacement allows the agent to determine accurate cognitive maps faster by using mathematical evaluation rather than physical exploration, reducing time loss while maintaining or improving accuracy.
3Adaptability or versatility
If multiple distinct states are perceptually aliased to the same observation, then the environment complexity increases, but conventional techniques cannot effectively distinguish between these states
Solution Approach 1:
The system applies segmentation by creating clones of observations for different contexts. When multiple distinct states are perceptually aliased to the same observation, the CSCG model segments this single observation into multiple cloned representations, each corresponding to a different underlying state. This allows the agent to handle aliased observations adaptively while maintaining a manageable level of complexity through structured cloning rather than exhaustive state representation.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating a graph model representing an environment being interacted with by an agent. In one aspect, one of the methods include: obtaining experience data; using the experience data to update a visitation count for each of one or more state-action pairs represented by the graph model; and at each of multiple environment exploration steps: computing a utility measure for each of the one or more state-action pairs represented by the graph model; determining, based on the utility measures, a sequence of one or more planned actions that have an information gain that satisfies a threshold; and controlling the agent to perform the sequence of one or more planned actions to cause the environment to transition from a state characterized by a last observation received after a last action in the experience data into a different state.