Sharif Digital Repository

Automated Planning is an active research area in Artificial Intelligence. In Classical planning, for simplicity, time is considered as the order of actions in plan. In temporal planning, due to the importance of time in real world problems, this simplifying assumption is not considered, and time is explicitly used in the planning process. Most of current methods for temporal planning are extensions of classical planning methods to include the explicit definition of time. Planning using Satisfiability is used as an efficient method to find optimal solutions for classical planning problems. In this dissertation, a temporal planner based on Satisfiability has been developed. This planner, as we... 

Automated planning is a branch of artiticial intelligence that studies intelligent agents’ decision making process. The objective is to design agents that are able to decide on their own, about how to perform tasks that are assigned to them. In the past 20 years, a popular and appealing method for solving planning problems has been to use satisfiability (SAT) techniques. In this method, the planning ptoblem with a preset length would be encoded into a satisfiability problem, which is then solved by a general satisfiability solver. The solution to the planning problem is then extracted from the solution of the SAT problem. The length of the problem is proportional to the number of steps in... 

Automated planning is a branch of Artificial intelligence aimed at obtaining plans (i.e. sequences of actions) for solving complex problems or for governing the behavior of intelligent agents. Reduction of planning problems to satisfiability problems is one of the most successful approaches to automated planning. In this method first the planning problem with a preset length is coded to a satisfiability problem. Then, a general SAT solver is used for solving the obtained SAT formula. To find the plan with optimum length, plans with increasing coding lengths are evaluated by a SAT solver in a sequential manner. Since much of descriptive information of planning problems is lost in converting... 

The so-called “Fast Downward” is a successful heuristic planner. This planner extracts informative data structures during planning. A number of efforts have been made to detect some constraints for guiding search toward the goal, in the hope to speed up the planning process. These constraints usually result in a number of sub-goals and determining their proper ordering. These sub-goals are called landmarks, which must be true at some point in every valid solution plan. Landmarks can be used to decompose a given planning task into several smaller sub-tasks. In this dissertation a new method is proposed to extracts landmark and recognizes their ordering based on fast downward basic data... 

Planning is one of the most important branches of artificial intelligence. The goal of planning is automated production of sequence of actions (plans) for some specified tasks. However the computation done by planners is very time consuming and complicated. Distributing computation is one solution to overcome such difficulties. Distribution divides the load of process between a number of agents and increases robustness. The purpose of this project is to reduce the computational overhead of planning by the means of distributing the planning process between a numbers of planners. In the proposed method, we first divide a classical planning problem to two separate subproblems by decomposition... 

Considering numerical values is an important step toward real world problems in planning. Although planning community has been aware of this fact since many years ago, but the complication involved in reasoning with numerical values made this challenge too difficult, thus very little and occasional research has been done on this issue.This dissertation is an effort to find an efficient method for solving numerical planning problems; in this regard, we use the “planning as satisfiability” approach. Planning as satisfiability is one of the most important and successful approaches for solving planning problems. Furthermore, developing SAT solvers with the capability of considering numerical... 

Planning, as one of the major area of the artificial intelligence, has substantial applications in different science and technology fields. In the classical planning, usually some restricted assumptions are made to simplify the planning procedure. For example, for the world representation, states and actions are defined precisely; however, in the real world such information is mostly inacurate. Therefore, in order to address this inacuracy, flexible planning has been developed. In this technique, robust restrictions such as complete satisfaction of preconditions are interpreted as preferences or priority. In other words, the flexible planning aims at fulfilling planning goals with maximum... 

Renewable resources, because of their compatibility with the environment, accessibility, and low cost of operation, have become the center of unprecedented attention. Since the output of these resources cannot be predicted accurately, they are usually implemented in microgrids with conventional generating units and energy storage systems.Furthermore, distribution networks are integral parts of power systems, and many studies have been conducted in order to improve them. Because of high investment and operation costs of distribution networks and environmental concerns, it is necessary to conduct meticulous economic studies so that with proper investments, profitable opportunities are provided...