Engineering Notes
Thoughts and Ideas on AI by Muthukrishnan
Tag: Planning
Landmark Based Planning Using Obligatory Subgoals to Guide Search
How landmark-based heuristics guide classical and LLM planners by identifying facts that must be true in every valid planReceding Horizon Planning and Plan Commitment in Agent Reasoning Loops
How agents manage the tradeoff between committing to a plan and staying responsive to new information using receding horizon planningDependency Aware Task Scheduling How Agents Execute Plans as Parallel DAGs
How agents represent plans as dependency graphs and execute independent tasks in parallel for better performance and fault toleranceAnytime Heuristic Search How Agents Find Good Plans Fast and Improve Them Over Time
Anytime heuristic search, including Weighted A* and ARA*, lets agents commit to a suboptimal plan immediately and refine it as time allows, with provable bounds on solution quality at every step.Classical Planning with STRIPS and the Delete Relaxation Heuristic
How STRIPS formalizes agent planning problems and how the delete relaxation trick produces powerful heuristics that guide search toward goals efficientlyCounterfactual Regret Minimization for Solving Imperfect Information Games
How CFR and its variants taught AI to master poker and navigate the fog of war in multi-agent decision makingModel-Based Reinforcement Learning How Agents Simulate Experience to Learn Faster
Explore how agents that build internal environment models can plan, simulate, and learn orders of magnitude faster than model-free approachesSuccessor Representations a Map of Where You Are Going
Understand successor representations — the elegant middle ground between model-free and model-based RL that enables fast adaptation and transfer across tasks.Active Inference and the Free Energy Principle How Agents Minimize Surprise Instead of Maximizing Reward
Explore Karl Friston's Free Energy Principle: a unified theory where agents minimize surprise through belief updating and action, offering an alternative foundation to reward-based reinforcement learningCausal Reasoning and Intervention Planning in AI Agents
How AI agents can move beyond correlation to understand cause and effect, enabling more robust planning, better tool use, and reliable interventions in the real worldDiffusion Models and Iterative Refinement in AI Agent Planning
Explore how diffusion models enable AI agents to generate and refine complex action plans through iterative denoising, revolutionizing long-horizon planning and decision-making.Beam Search and Parallel Decoding for Multiple Solution Paths in AI Agents
Master beam search—a powerful technique for exploring multiple solution paths simultaneously in AI agents, from classical NLP to modern LLM reasoning systems.Constraint Satisfaction Problems for Valid Solutions in Complex Agent Planning
Master constraint satisfaction problems (CSP) - a fundamental technique for agent planning, scheduling, and configuration tasks where finding any valid solution is the goal.Planning with Backtracking and Replanning for Resilient Adaptive Agents
Learn how backtracking and replanning enable AI agents to recover from failures and adapt to dynamic environmentsDecision-Making Under Partial Observability with POMDPs
**Partially Observable Markov Decision Processes (POMDPs)** are the mathematical framework for making optimal decisions...Building Rational Agents with Human-Like Reasoning Using BDI Architecture
Master the BDI architecture pattern that models rational agent behavior through beliefs, desires, and intentions—a bridge between philosophy and practical AI systems.Goal-Oriented Action Planning for Dynamic Problem Solving in Adaptive Agents
Discover how Goal-Oriented Action Planning (GOAP) enables AI agents to dynamically create flexible plans that adapt to changing conditions, from game NPCs to modern autonomous systems.Hierarchical Task Networks for Planning with Decomposition
## Concept Introduction **Hierarchical Task Networks (HTN)** planning works by decomposing abstract goals into progres...Monte Carlo Tree Search from Games to LLMs
## Concept Introduction **Monte Carlo Tree Search (MCTS)** is a heuristic search algorithm for finding optimal decisio...Improving Agent Reliability with Plan-and-Solve Prompting
## Concept Introduction **Plan-and-Solve (PS) Prompting** is a structured approach to AI agent reliability. Instead of...