Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Abstract ethical principles (like beneficence and privacy) are often highly context-dependent. Principles2Plan uses LLMs to interpret these principles within a given planning problem, generating specific, actionable rules that guide automated planning. This reduces the significant manual effort traditionally required to encode such rules, making ethical AI more practical.
The system empowers users (domain experts or AI ethicists) to review, refine, and prioritize the LLM-generated ethical rules. This collaborative approach ensures that the final rules align with human judgment and nuanced ethical considerations, combining the LLM's generative power with human oversight for robust, transparent ethical planning.
Principles2Plan translates the refined ethical rules into PDDL-Ethical code, which includes action costs to represent ethical implications. This code is then processed by a classical planner, resulting in plans that not only achieve goals but also adhere to specified ethical constraints. The system provides a side-by-side comparison of plans with and without ethical rules.
Enterprise Process Flow
Input Domain, Problem, & Principles
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LLM Generates Context-Specific Ethical Rules
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User Reviews & Prioritises Rules (Human Oversight)
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LLM Generates PDDL-Ethical Code
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Code Transpiled & Sent to Classical Planner
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Ethically-Informed Plan Output
82.2%
PDDL Code Generation Success Rate (Zhong et al. 2025)
| Feature | Principles2Plan (LLM-Guided) | Traditional (Manual Encoding) |
|---|---|---|
| Rule Generation |
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| Adaptability |
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| Transparency |
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Autonomous Vehicle Scenario
In an autonomous vehicle scenario, Principles2Plan successfully generated context-specific ethical rules based on beneficence and privacy principles. For instance, a passenger needing urgent medical attention could justify taking an unauthorised shortcut, while a leisure trip would prioritize standard traffic rules to avoid unnecessary risk. This demonstrates how the system's human-LLM collaboration model can navigate nuanced ethical dilemmas, producing an ethically-informed plan that balances safety and responsiveness.
Quantify Your AI Impact
Use our interactive calculator to estimate the potential ROI and efficiency gains for your enterprise.
Annual Savings Potential
$0
Hours Reclaimed Annually
0
Your AI Implementation Roadmap
A phased approach to integrate advanced AI into your enterprise, ensuring sustainable growth and ethical deployment.
Phase 1: Discovery & Strategy
Comprehensive analysis of existing workflows, identification of AI opportunities, and strategic alignment with business objectives. Ethical framework established.
Phase 2: Pilot Development & Testing
Rapid prototyping of AI solutions, iterative testing, and fine-tuning based on real-world data and user feedback. Initial ethical rules operationalised.
Phase 3: Full-Scale Deployment
Seamless integration of validated AI systems across the enterprise, robust monitoring, and continuous performance optimization. Ethical monitoring in place.
Phase 4: Optimization & Expansion
Ongoing evaluation of AI impact, identification of new application areas, and scaling of solutions for maximum long-term value. Ethical framework regularly reviewed.
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