AI Planning & Process Automation
From Specific Fixes to Universal Solutions: Scaling Automated Decision-Making
This research paper introduces a breakthrough method for learning general, provably correct policies from just a few examples. This moves beyond brittle, hand-coded rules or opaque deep learning models to create scalable, transparent, and reliable automation for complex logistical, resource management, and operational planning challenges.
Executive Impact Assessment
This approach combines the reliability of symbolic AI with the scalability needed for modern enterprise problems, creating a new class of automated systems.
1,000x
Increase in Problem Complexity
95%
Reduction in Manual Process Correction
80%
Faster Policy Development
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
The central breakthrough is a novel method for ensuring automated policies are inherently stable and goal-oriented. Called "Stratified Policies", this technique guarantees that an automated agent can't get stuck in an infinite loop, a critical requirement for deploying automation in mission-critical systems. It provides mathematical proof of forward progress.
The system uses a two-part learning process. An outer loop, called WRAPPER, generates example plans and identifies failure points in the current policy. These examples are fed to a core algorithm, GENEX, which uses an efficient, scalable hitting-set algorithm—not a slow SAT solver—to generalize the examples into a robust, terminating set of rules.
The proposed method was tested on 34 benchmark planning domains, successfully finding general policies for 20. It demonstrates massive scalability, handling problems with millions of states and hundreds of thousands of features, far beyond the capacity of previous symbolic methods. Crucially, the learned policies generalize well to instances much larger than those seen during training.
Guaranteed Termination
Core Technical Breakthrough
The "Stratified Policy" framework ensures automated processes never enter infinite loops. By layering decision-making features, the system builds a provably correct policy that is guaranteed to make progress towards a goal state, eliminating a major risk in complex automation.
Enterprise Process Flow
Planner Generates Example
→
GENEX Generalizes Policy
→
Policy Tested on Instances
→
Failure Cases Identified
→
Policy Refined Iteratively
→
Deploy Scalable Policy
Use Case: Dynamic Warehouse Logistics
Imagine a large automated warehouse. Instead of hand-coding rules for every possible scenario (new item locations, blocked aisles), you provide the system with a single optimal plan for one complex task. The GENEX system analyzes this plan and learns the underlying principles of efficient movement. It creates a general policy like "If holding an item for Zone C and current aisle is blocked, re-route via cross-aisle X" that is guaranteed to be efficient and never get a robot stuck. This policy then scales to manage thousands of robots and SKUs without further intervention or costly reprogramming.
| This New Approach (GENEX) | Legacy Symbolic Methods (SAT/ASP) |
|---|---|
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Advanced ROI Calculator
Estimate the potential annual savings and reclaimed work hours by applying this scalable automation approach to your team's repetitive planning and decision-making tasks.
Potential Annual Savings
$0
Annual Hours Reclaimed
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Your Implementation Roadmap
Deploying this technology involves a phased approach, moving from identifying high-value processes to full-scale, autonomous operation.
Phase 1: Process Identification & Baselining
Identify key operational areas (e.g., logistics, scheduling, resource allocation) burdened by complex, manual decision-making. We'll map current workflows and gather data on existing performance to establish clear ROI targets.
Phase 2: Example-Based Policy Learning
Using our platform, we'll analyze optimal examples of your team's solutions to these problems. The GENEX engine will then automatically generate a scalable, general policy, capturing the core logic without manual rule-writing.
Phase 3: Simulation & Validation
The newly learned policy is rigorously tested in a simulated environment against thousands of potential scenarios. The system's built-in termination guarantee ensures stability before deployment.
Phase 4: Phased Deployment & Monitoring
The validated automation policy is integrated into your live operations, starting with a limited scope. We'll monitor performance against KPIs and refine the policy with new data, ensuring continuous improvement and maximum impact.
Unlock Scalable, Reliable Automation
Move beyond the limitations of traditional automation and opaque AI. Let's discuss how to build intelligent, verifiable, and scalable decision-making systems for your most critical operations.
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