1. Pednault (2002) introduced the concept of using a planning graph as the basis for deriving heuristics for plan synthesis by state space and CSP search.2. Rintanen (1994) proposed ADL and the state-transition model of action, which provides a framework for representing and reasoning about actions in a domain.3. Refanidis and Vlahavas (2001) described the GRT planning system, which uses backward heuristic construction in forward state-space planning.4. Ruml, Do, and Fromherz (2004) proposed distance estimates for planning in the discrete belief space, which can be used to improve the efficiency of planning algorithms.5. Sanchez, Do, and Fromherz (2005) developed on-line planning and scheduling techniques for high-speed manufacturing, which can help improve production efficiency.6. Sanchez and Kambhampati (2005) proposed planning graph heuristics for selecting objectives in over-subscription planning problems, which can help optimize resource allocation.7. Sanchez and Mali (2003) introduced S-Mep, a planner for numeric goals that uses a combination of forward and backward chaining to generate plans.8. Smith (1999) proposed temporal planning with mutual exclusion reasoning, which can help handle tasks with conflicting constraints.9. Smith (2004) discussed choosing objectives in over-subscription planning, which involves selecting the most important objectives to maximize efficiency.10. Kambhampati and Zimmerman (2005) proposed planning graph heuristics for partially ordered domains, which can handle complex relationships between tasks.11. Smith (2005) introduced approximate planning with abstract state spaces, which can help reduce the computational complexity of planning algorithms.12. Do, Sanchez, and Fromherz (2005) proposed planning graph heuristics for solving traveling salesman problems, which can help optimize routes in logistics and transportation.13. Zimmerman and Kambhampati (2006) introduced reachability-based planning with uncertainty, which can handle uncertain scheduling constraints.14. Kambhampati and Do (2005) proposed planning graph heuristics for multi-agent systems, which can help coordinate actions among multiple agents.15. Zimmerman and Kambhampati (2006) discussed planning graph heuristics for real-time systems, which can help ensure timely completion of tasks in critical systems.These articles demonstrate the versatility and applicability of planning graph heuristics across different domains and problem types.

1. Pednault (2002) introduced the concept of using a planning graph as the basis for deriving heuristics for plan synthesis by state space and CSP search.2. Rintanen (1994) proposed ADL and the state-transition model of action, which provides a framework for representing and reasoning about actions in a domain.3. Refanidis and Vlahavas (2001) described the GRT planning system, which uses backward heuristic construction in forward state-space planning.4. Ruml, Do, and Fromherz (2004) proposed distance estimates for planning in the discrete belief space, which can be used to improve the efficiency of planning algorithms.5. Sanchez, Do, and Fromherz (2005) developed on-line planning and scheduling techniques for high-speed manufacturing, which can help improve production efficiency.6. Sanchez and Kambhampati (2005) proposed planning graph heuristics for selecting objectives in over-subscription planning problems, which can help optimize resource allocation.7. Sanchez and Mali (2003) introduced S-Mep, a planner for numeric goals that uses a combination of forward and backward chaining to generate plans.8. Smith (1999) proposed temporal planning with mutual exclusion reasoning, which can help handle tasks with conflicting constraints.9. Smith (2004) discussed choosing objectives in over-subscription planning, which involves selecting the most important objectives to maximize efficiency.10. Kambhampati and Zimmerman (2005) proposed planning graph heuristics for partially ordered domains, which can handle complex relationships between tasks.11. Smith (2005) introduced approximate planning with abstract state spaces, which can help reduce the computational complexity of planning algorithms.12. Do, Sanchez, and Fromherz (2005) proposed planning graph heuristics for solving traveling salesman problems, which can help optimize routes in logistics and transportation.13. Zimmerman and Kambhampati (2006) introduced reachability-based planning with uncertainty, which can handle uncertain scheduling constraints.14. Kambhampati and Do (2005) proposed planning graph heuristics for multi-agent systems, which can help coordinate actions among multiple agents.15. Zimmerman and Kambhampati (2006) discussed planning graph heuristics for real-time systems, which can help ensure timely completion of tasks in critical systems.These articles demonstrate the versatility and applicability of planning graph heuristics across different domains and problem types.