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Good day Sir,
Thank you for your invitation.
I have no knowledge what learning-based planning means.
The individualized learning plan is not a one-time activity but an ongoing process by which the student defines, explores, and then refines his or her interests and goals throughout high school. I think learning based planning is similar to it.
.Automated Planning (AP) studies the generation of action sequences for problem solving. A problem in AP is defined by a state-transition function describing the dynamics of the world, the initial state of the world and the goals to be achieved. According to this definition, AP problems seem to be easily tackled by searching for a path in a graph, which is a well-studied problem.
The languages for representing automated planning AP tasks are typically based on extensions of first-order logic. They encode tasks using a set of actions that represents the state-transition function of the world (the planning domain) and a set of first-order predicates that represent the initial state together with the goals of the AP task (the planning problem). In the early days of AP, STRIPS was the most popular representation language. In1998 the Planning Domain Definition Language (PDDL) was developed for the first International Planning Competition (IPC) and since that date it has become the standard language for the AP community. In PDDL (Fox & Long,2003), an action in the planning domain is represented by: (1) the action preconditions, a list of predicates indicating the facts that must be true so the action becomes applicable and (2) the action post -conditions, typically separated in add and delete lists, which are lists of predicates indicating the changes in the state after the action is applied.
Before the mid '90s, automated planners could only synthesize plans of no more than10 actions in an acceptable amount of time. During those years, planners strongly depended on speedup techniques for solving AP problems. Therefore, the application of search control became a very popular solution to accelerate planning algorithms. In the late90's, a significant scale-up in planning took place due to the appearance of the reachability planning graphs (Blum & Furst,1995) and the development of powerful domain independent heuristics (Hoffman & Nebel,2001) (Bonet & Geffner,2001). Planners using these approaches could often synthesize100-action plans just in seconds.
Automated Planning (AP) studies the generation of action sequences for problem solving. A problem in AP is defined by a state-transition function describing the dynamics of the world, the initial state of the world and the goals to be achieved. According to this definition, AP problems seem to be easily tackled by searching for a path in a graph, which is a well-studied problem. However, the graphs resulting from AP problems are so large that explicitly specifying them is not feasible. Thus, different approaches have been tried to address AP problems. Since the mid90’s, new planning algorithms have enabled the solution of practical-size AP problems. Nevertheless, domain-independent planners still fail in solving complex AP problems, as solving planning tasks is a PSPACE-Complete problem (Bylander,94). How do humans cope with this planning-inherent complexity? One answer is that our experience allows us to solve problems more quickly; we are endowed with learning skills that help us plan when problems are selected from a stable population. Inspire by this idea, the field of learning-based planning studies the development of AP systems able to modify their performance according to previous experiences. Since the first days, Artificial Intelligence (AI) has been concerned with the problem of Machine Learning (ML). As early as1959, Arthur L. Samuel developed a prominent program that learned to improve its play in the game of checkers (Samuel,1959). It is hardly surprising that ML has often been used to make changes in systems that perform tasks associated with AI, such as perception, robot control or AP. This article analyses the diverse ways ML can be used to improve AP processes. First, we review the major AP concepts and summarize the main research done in learning-based planning. Second, we describe current trends in applying ML to AP. Finally, we comment on the next avenues for combining AP and ML and conclude.
How to actuating, controlling and organizing the Business firm, that we called PLANNING.
The students under the supervision of their teacher put the plan and discuss the details of the objectives and the colors of activity and find out details of the plan
I fully endorse the answer of Mr. Alex Al Yazouri
