The Hierarchical Neuro-Symbolic Decision Transformer is a framework that combines classical symbolic planning with transformer-based policies to tackle complex decision-making tasks. At the high level, a symbolic planner constructs a sequence of operators based on logical propositions, ensuring adherence to global constraints. At the low level, each operator is translated into a sub-goal token that conditions a decision transformer to generate actions in uncertain environments. This approach addresses approximation errors from both the symbolic planner and the neural execution layer, outperforming purely neural approaches in tasks like grid-worlds with multiple keys and locked doors.
Symbolic Planning, Transformer Networks
Hierarchical Neuro-Symbolic Framework
Grid-world environments
Success Rate, Policy Efficiency
Cloud-based, On-premises
Yes
Yes
Hierarchical, Interpretable, Efficient
Yes
High-performance GPUs
Linux, Windows, macOS
Compatible with symbolic and neural systems
Secure computation, Access control
GDPR
None
No
Active research community
AI researchers, Robotics experts
Gigabytes
Low
Moderate
High
Bias in decision-making
Complexity in large environments
Robotics, Gaming, Autonomous Vehicles
Autonomous navigation, Task planning, Game strategy
Robotics companies, Game developers
APIs, SDKs
Scalable
Technical support, Community forums
99.9% uptime
Command-line, Web-based
Yes
Available in multiple languages
Subscription-based
Yes
Technology partners
Pending
Compliant with major regulations
1.0
SaaS
Yes
RESTful API
B2B
0.00
USD
Commercial
01/03/2023
01/10/2023
+1-800-555-0199
Supports complex decision-making, Integrates symbolic and neural methods
Yes