Automated planning and scheduling

Automated planning and scheduling, sometimes denoted as simply AI planning, is a branch of artificial intelligence that concerns the realization of strategies or action sequences, typically for execution by intelligent agents, autonomous robots and unmanned vehicles. Unlike classical control and classification problems, the solutions are complex and must be discovered and optimized in multidimensional space. Planning is also related to decision theory. In known environments with available models, planning can be done offline. Solutions can be found and evaluated prior to execution. In dynamically unknown environments, the strategy often needs to be revised online. Models and policies must be adapted. Solutions usually resort to iterative trial and error processes commonly seen in artificial intelligence. These include dynamic programming, reinforcement learning and combinatorial optimization. Languages used to describe planning and scheduling are often called action languages. Given a description of the possible initial states of the world, a description of the desired goals, and a description of a set of possible actions, the planning problem is to synthesize a plan that is guaranteed (when applied to any of the initial states) to generate a state which contains the desired goals (such a state is called a goal state). The difficulty of planning is dependent on the simplifying assumptions employed. Several classes of planning problems can be identified depending on the properties the problems have in several dimensions. Are the actions deterministic or non-deterministic? For nondeterministic actions, are the associated probabilities available? Are the state variables discrete or continuous? If they are discrete, do they have only a finite number of possible values? Can the current state be observed unambiguously? There can be full observability and partial observability.

Automated planning and scheduling

Scalable constrained optimization

Scalable Logic Rewriting Using Don’t Cares

Learning to Remove Cuts in Integer Linear Programming

Learning to Remove Cuts in Integer Linear Programming

Scalable constrained optimization

Scalable Logic Rewriting Using Don’t Cares