Teamcore Research Group.
Scheduling. Automated planning and scheduling. Automated planning and scheduling 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. Overview[edit] 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 find a plan that is guaranteed (from any of the initial states) to generate a sequence of actions that leads to one of the goal states. Lists. Hierarchical task network. In artificial intelligence, the hierarchical task network, or HTN, is an approach to automated planning in which the dependency among actions can be given in the form of networks.
Planning problems are specified in the hierarchical task network approach by providing a set of tasks, which can be: primitive tasks, which roughly correspond to the actions of STRIPS;compound tasks, which can be seen as composed of a set of simpler tasks;goal tasks, which roughly corresponds to the goals of STRIPS, but are more general. A primitive task is an action that can be executed.
A compound task is a complex task composed of a sequence of actions. A goal task is a task of satisfying a condition. Constraints among tasks are expressed in form of networks, called task networks. A task network can for example specify that a condition is necessary for a primitive action to be executed. The best-known domain-independent HTN-planning software is: See also[edit] References[edit]
Combinatorial optimization. In applied mathematics and theoretical computer science, combinatorial optimization is a topic that consists of finding an optimal object from a finite set of objects.[1] In many such problems, exhaustive search is not feasible. It operates on the domain of those optimization problems, in which the set of feasible solutions is discrete or can be reduced to discrete, and in which the goal is to find the best solution.
Some common problems involving combinatorial optimization are the traveling salesman problem ("TSP") and the minimum spanning tree problem ("MST"). Combinatorial optimization is a subset of mathematical optimization that is related to operations research, algorithm theory, and computational complexity theory. It has important applications in several fields, including artificial intelligence, machine learning, mathematics, auction theory, and software engineering. Applications[edit] Applications for combinatorial optimization include, but are not limited to: Methods[edit] Others. Travelling salesman problem. The travelling salesman problem (TSP) asks the following question: Given a list of cities and the distances between each pair of cities, what is the shortest possible route that visits each city exactly once and returns to the origin city?
It is an NP-hard problem in combinatorial optimization, important in operations research and theoretical computer science. Solution of a travelling salesman problem TSP is a special case of the travelling purchaser problem. In the theory of computational complexity, the decision version of the TSP (where, given a length L, the task is to decide whether the graph has any tour shorter than L) belongs to the class of NP-complete problems. Thus, it is possible that the worst-case running time for any algorithm for the TSP increases superpolynomially (perhaps, specifically, exponentially) with the number of cities. The problem was first formulated in 1930 and is one of the most intensively studied problems in optimization.
History[edit] Richard M. Since For are. Minimum spanning tree. The only minimum spanning tree of a planar graph. Each edge is labeled with its weight, which here is roughly proportional to its length. One example would be a telecommunications company laying cable to a new neighborhood. If it is constrained to bury the cable only along certain paths, then there would be a graph representing which points are connected by those paths. Some of those paths might be more expensive, because they are longer, or require the cable to be buried deeper; these paths would be represented by edges with larger weights. A spanning tree for that graph would be a subset of those paths that has no cycles but still connects to every house. Properties[edit] Possible multiplicity[edit] This figure shows there may be more than one minimum spanning tree in a graph. Uniqueness[edit] If each edge has a distinct weight then there will be only one, unique minimum spanning tree. A proof of uniqueness by contradiction is as follows.
Minimum-cost subgraph[edit] Cycle property[edit] Reinforcement learning. Reinforcement learning is an area of machine learning inspired by behaviorist psychology, concerned with how software agents ought to take actions in an environment so as to maximize some notion of cumulative reward. The problem, due to its generality, is studied in many other disciplines, such as game theory, control theory, operations research, information theory, simulation-based optimization, statistics, and genetic algorithms.
In the operations research and control literature, the field where reinforcement learning methods are studied is called approximate dynamic programming. The problem has been studied in the theory of optimal control, though most studies there are concerned with existence of optimal solutions and their characterization, and not with the learning or approximation aspects. In economics and game theory, reinforcement learning may be used to explain how equilibrium may arise under bounded rationality. Introduction[edit] The rules are often stochastic. . To policy . Nonlin Planning System. The Nonlin hierarchical partial-order AI planning system, developed by Austin Tate at the University of Edinburgh, is available in a browsable and downloadable form here: or thanks to Aaron Sloman at the University of Birmingham for encouraging Austin to make it it run as it used to in a much earlier version of Poplog, so that it works in both Windows Poplog and Linux/Unix Poplog.
Get a copy of the freely distributable version of Poplog at [PC/Windows version 15.5 is available locally here - note this version is only for a 32 bit Windows OS]. Unpack the files to a temporary location and run "SETUP.EXE". It may be best to allow the installer to place the files in the default sugegsted position to avoid any problems with spaces in directory names. You can then run Poplog POP-11 in a Window via the "Pop-11" shortcut created. Documentation UM Nonlin. Dynamic programming.
In mathematics, computer science, economics, and bioinformatics, dynamic programming is a method for solving complex problems by breaking them down into simpler subproblems. It is applicable to problems exhibiting the properties of overlapping subproblems[1] and optimal substructure (described below). When applicable, the method takes far less time than naive methods that don't take advantage of the subproblem overlap (like depth-first search). The idea behind dynamic programming is quite simple. In general, to solve a given problem, we need to solve different parts of the problem (subproblems), then combine the solutions of the subproblems to reach an overall solution.
Often when using a more naive method, many of the subproblems are generated and solved many times. Dynamic programming algorithms are used for optimization (for example, finding the shortest path between two points, or the fastest way to multiply many matrices). History[edit] "I spent the Fall quarter (of 1950) at RAND. O-Plan.
Education.