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How to use Automated Planning
How to use Automated Planning
Published: 2014/11/02
Channel: planning-tools.net
ICAPS 2014: Tutorial by Scott Sanner on Decision Diagrams in Automated Planning and Scheduling
ICAPS 2014: Tutorial by Scott Sanner on Decision Diagrams in Automated Planning and Scheduling
Published: 2015/06/03
Channel: ICAPS
Automated planning and scheduling
Automated planning and scheduling
Published: 2016/01/22
Channel: WikiAudio
Tutorial: Introduction to AI Planning, Part 1 (Amanda Coles, EASSS 2013)
Tutorial: Introduction to AI Planning, Part 1 (Amanda Coles, EASSS 2013)
Published: 2013/07/07
Channel: Andrew
AUTOMATED MRO PLANNING AND SCHEDULING WEBINAR
AUTOMATED MRO PLANNING AND SCHEDULING WEBINAR
Published: 2017/07/17
Channel: Realization Technologies
Automated Planning for Configuration Changes
Automated Planning for Configuration Changes
Published: 2011/12/10
Channel: Herry Herry
ICAPS 2015: "Planning-Based Reasoning for Automated Large-Scale Data Analysis"
ICAPS 2015: "Planning-Based Reasoning for Automated Large-Scale Data Analysis"
Published: 2016/03/06
Channel: ICAPS
Lecture - 18 Partial Order Planning
Lecture - 18 Partial Order Planning
Published: 2008/04/30
Channel: nptelhrd
Production Planning & Scheduling with Excel #10 - Manual vs. Automated Scheduling
Production Planning & Scheduling with Excel #10 - Manual vs. Automated Scheduling
Published: 2015/04/20
Channel: Martin Mirsky
Experiment 3 - Solving Multi-agent Planning Tasks by Using Automated Planning
Experiment 3 - Solving Multi-agent Planning Tasks by Using Automated Planning
Published: 2016/06/22
Channel: PLGGroupUC3M
ICAPS 2012: Session VIb on "Planning and Scheduling for Shipping and Transportation"
ICAPS 2012: Session VIb on "Planning and Scheduling for Shipping and Transportation"
Published: 2014/08/18
Channel: ICAPS
Nao robot solving a pick up task using Automated Planning and Computed Vision (Experiment 2A)
Nao robot solving a pick up task using Automated Planning and Computed Vision (Experiment 2A)
Published: 2014/09/24
Channel: PLGGroupUC3M
Experiment 1 - Solving Multi-agent Planning Tasks by Using Automated Planning
Experiment 1 - Solving Multi-agent Planning Tasks by Using Automated Planning
Published: 2016/06/22
Channel: PLGGroupUC3M
Steve Chien: "Automated Scheduling for Rosetta Science Operations" | Talks at Google
Steve Chien: "Automated Scheduling for Rosetta Science Operations" | Talks at Google
Published: 2016/10/17
Channel: Talks at Google
Experiment 3 - Solving Multi-agent Planning Tasks by Using Automated Planning (Speed up)
Experiment 3 - Solving Multi-agent Planning Tasks by Using Automated Planning (Speed up)
Published: 2016/07/05
Channel: PLGGroupUC3M
Nao robot solving a pick up task using Automated Planning and Computed Vision (Experiment 2B)
Nao robot solving a pick up task using Automated Planning and Computed Vision (Experiment 2B)
Published: 2014/09/24
Channel: PLGGroupUC3M
Experiment 2 - Solving Multi-agent Planning Tasks by Using Automated Planning
Experiment 2 - Solving Multi-agent Planning Tasks by Using Automated Planning
Published: 2016/06/22
Channel: PLGGroupUC3M
Experiment 4 - Solving Multi-agent Planning Tasks by Using Automated Planning
Experiment 4 - Solving Multi-agent Planning Tasks by Using Automated Planning
Published: 2016/06/22
Channel: PLGGroupUC3M
AI Planning in Sale & Distribution
AI Planning in Sale & Distribution
Published: 2014/01/26
Channel: Caesar Julius
Unit 8 16 Classical Planning 1
Unit 8 16 Classical Planning 1
Published: 2011/11/01
Channel: knowitvideos
ICAPS 2012: Session IVa "Planning and Scheduling in the Real World"
ICAPS 2012: Session IVa "Planning and Scheduling in the Real World"
Published: 2014/08/18
Channel: ICAPS
Real--Time UAV Maneuvering via Automated Planning in Multi-Agent Simulation
Real--Time UAV Maneuvering via Automated Planning in Multi-Agent Simulation
Published: 2017/05/03
Channel: Miquel Ramírez
ICAPS 2014: Martin Suda on "Property Directed Reachability for Automated Planning"
ICAPS 2014: Martin Suda on "Property Directed Reachability for Automated Planning"
Published: 2015/05/05
Channel: ICAPS
Aeromark Automated Job Scheduling Demo
Aeromark Automated Job Scheduling Demo
Published: 2011/11/11
Channel: Aero Mark
Implementing an automated scheduling tool
Implementing an automated scheduling tool
Published: 2011/06/23
Channel: Steve Ridley
RADAR - Proactive Decision Support using Automated AI Planning
RADAR - Proactive Decision Support using Automated AI Planning
Published: 2017/03/22
Channel: Sailik Sengupta
AI Planning in Medicine
AI Planning in Medicine
Published: 2013/01/31
Channel: arturogff
Real--Time UAV Maneuvering via Automated Planning in Multi-Agent Simulations (Slow)
Real--Time UAV Maneuvering via Automated Planning in Multi-Agent Simulations (Slow)
Published: 2017/05/04
Channel: Miquel Ramírez
AI Planning Project
AI Planning Project
Published: 2014/03/03
Channel: Chris Esposo
ICAPS 2015: "Constraint Modeling for Planning"
ICAPS 2015: "Constraint Modeling for Planning"
Published: 2016/03/06
Channel: ICAPS
ICAPS 2012: Session Ia on "Temporal Planning & Scheduling"
ICAPS 2012: Session Ia on "Temporal Planning & Scheduling"
Published: 2014/08/18
Channel: ICAPS
ICAPS 2013: Matthew Crosby - Automated Agent Decomposition for Classical Planning
ICAPS 2013: Matthew Crosby - Automated Agent Decomposition for Classical Planning
Published: 2014/07/03
Channel: ICAPS 2013 - 23rd International Conference on Automated Planning and Scheduling
Evolutionary AI Planning - DAEx
Evolutionary AI Planning - DAEx
Published: 2013/02/23
Channel: Yann Semet
AI Planning in Space Science : Rosetta
AI Planning in Space Science : Rosetta
Published: 2013/02/24
Channel: Manuel Fernández
ICAPS 2014: Simon Parkinson on "Automated Planning for Multi-Objective Machine Tool ..."
ICAPS 2014: Simon Parkinson on "Automated Planning for Multi-Objective Machine Tool ..."
Published: 2015/06/01
Channel: ICAPS
AI Planning for Space Supplement
AI Planning for Space Supplement
Published: 2013/02/10
Channel: Ai Austin
Bob Foster AI Planning creative challenge
Bob Foster AI Planning creative challenge
Published: 2015/05/09
Channel: Robert Foster
ICAPS 2015: "Speeding-Up Any-Angle Path-Planning on Grids"
ICAPS 2015: "Speeding-Up Any-Angle Path-Planning on Grids"
Published: 2016/03/06
Channel: ICAPS
ICAPS 2015: "ROSPlan: Planning in the Robot Operating System"
ICAPS 2015: "ROSPlan: Planning in the Robot Operating System"
Published: 2016/03/06
Channel: ICAPS
Automated Planning Theory & Practice The Morgan Kaufmann Series in Artificial Intelligence
Automated Planning Theory & Practice The Morgan Kaufmann Series in Artificial Intelligence
Published: 2017/05/08
Channel: Fiodor
ICAPS 2016: Planning Competition for Logistics Robots in Simulation
ICAPS 2016: Planning Competition for Logistics Robots in Simulation
Published: 2016/10/13
Channel: ICAPS
ICAPS 2015: "LP-based Heuristics for Cost-optimal Classical Planning"
ICAPS 2015: "LP-based Heuristics for Cost-optimal Classical Planning"
Published: 2016/03/06
Channel: ICAPS
ICAPS 2014: Aijun Bai on "Thompson Sampling Based Monte-Carlo Planning in POMDPs"
ICAPS 2014: Aijun Bai on "Thompson Sampling Based Monte-Carlo Planning in POMDPs"
Published: 2014/07/03
Channel: ICAPS
ICAPS 2015: "A Normal Form for Classical Planning Tasks"
ICAPS 2015: "A Normal Form for Classical Planning Tasks"
Published: 2016/03/06
Channel: ICAPS
AI planning:Swarm Intelligence
AI planning:Swarm Intelligence
Published: 2014/02/25
Channel: Kieran Moore
ICAPS 2013: Masood F. Rankooh - New Encoding Methods for SAT-Based Temporal Planning
ICAPS 2013: Masood F. Rankooh - New Encoding Methods for SAT-Based Temporal Planning
Published: 2014/07/03
Channel: ICAPS 2013 - 23rd International Conference on Automated Planning and Scheduling
ICAPS 2013: Ran Taig - Compiling Conformant Probabilistic Planning Problems into Classical Planning
ICAPS 2013: Ran Taig - Compiling Conformant Probabilistic Planning Problems into Classical Planning
Published: 2014/06/21
Channel: ICAPS 2013 - 23rd International Conference on Automated Planning and Scheduling
ICAPS 2012: DC Posters & System Demos
ICAPS 2012: DC Posters & System Demos
Published: 2014/08/18
Channel: ICAPS
ICAPS 2013: Tony T. Tran - Hybrid Queueing Theory and Scheduling Models for Dynamic Environments ...
ICAPS 2013: Tony T. Tran - Hybrid Queueing Theory and Scheduling Models for Dynamic Environments ...
Published: 2014/07/03
Channel: ICAPS 2013 - 23rd International Conference on Automated Planning and Scheduling
Automated Planning Theory  & Practice The Morgan Kaufmann Series in Artificial Intelligence
Automated Planning Theory & Practice The Morgan Kaufmann Series in Artificial Intelligence
Published: 2015/10/07
Channel: Linette 2
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WIKIPEDIA ARTICLE

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Automated planning and scheduling, sometimes denoted as simply AI Planning,[1] 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.

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 synthesise 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 nondeterministic? 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.
  • How many initial states are there, finite or arbitrarily many?
  • Do actions have a duration?
  • Can several actions be taken concurrently, or is only one action possible at a time?
  • Is the objective of a plan to reach a designated goal state, or to maximize a reward function?
  • Is there only one agent or are there several agents? Are the agents cooperative or selfish? Do all of the agents construct their own plans separately, or are the plans constructed centrally for all agents?

The simplest possible planning problem, known as the Classical Planning Problem, is determined by:

  • a unique known initial state,
  • durationless actions,
  • deterministic actions,
  • which can be taken only one at a time,
  • and a single agent.

Since the initial state is known unambiguously, and all actions are deterministic, the state of the world after any sequence of actions can be accurately predicted, and the question of observability is irrelevant for classical planning.

Further, plans can be defined as sequences of actions, because it is always known in advance which actions will be needed.

With nondeterministic actions or other events outside the control of the agent, the possible executions form a tree, and plans have to determine the appropriate actions for every node of the tree.

Discrete-time Markov decision processes (MDP) are planning problems with:

  • durationless actions,
  • nondeterministic actions with probabilities,
  • full observability,
  • maximization of a reward function,
  • and a single agent.

When full observability is replaced by partial observability, planning corresponds to partially observable Markov decision process (POMDP).

If there are more than one agent, we have multi-agent planning, which is closely related to game theory.

Domain Independent Planning[edit]

In AI Planning, planners typically input a domain model (a description of a set of possible actions which model the domain) as well as the specific problem to be solved specified by the initial state and goal. Such planners are called "Domain Independent" to emphasis the fact that they can solve planning problems from a wide range of domains. Typical examples of domains are block stacking, logistics, workflow management, and robot task planning. Hence a single domain independent planner can be used to solve planning problems in all these various domains. On the other hand, a route planner is typical of a domain specific planner.

Planning Domain Modelling Languages[edit]

The most commonly used languages for representing planning domains and specific planning problems, such as STRIPS and PDDL for Classical Planning, are based on state variables. Each possible state of the world is an assignment of values to the state variables, and actions determine how the values of the state variables change when that action is taken. Since a set of state variables induce a state space that has a size that is exponential in the set, planning, similarly to many other computational problems, suffers from the curse of dimensionality and the combinatorial explosion.

An alternative language for describing planning problems is that of hierarchical task networks, in which a set of tasks is given, and each task can be either realized by a primitive action or decomposed into a set of other tasks. This does not necessarily involve state variables, although in more realistic applications state variables simplify the description of task networks.

Algorithms for planning[edit]

Classical planning[edit]

Reduction to other problems[edit]

Temporal planning[edit]

Temporal planning can be solved with methods similar to classical planning. The main difference is, because of the possibility of several, temporally overlapping actions with a duration being taken concurrently, that the definition of a state has to include information about the current absolute time and how far the execution of each active action has proceeded. Further, in planning with rational or real time, the state space may be infinite, unlike in classical planning or planning with integer time. Temporal planning is closely related to scheduling problems. Temporal planning can also be understood in terms of timed automata.

Probabilistic planning[edit]

Probabilistic planning can be solved with iterative methods such as value iteration and policy iteration, when the state space is sufficiently small. With partial observability, probabilistic planning is similarly solved with iterative methods, but using a representation of the value functions defined for the space of beliefs instead of states.

Preference-based planning[edit]

In preference-based planning, the objective is not only to produce a plan but also to satisfy user-specified preferences. A difference to the more common reward-based planning, for example corresponding to MDPs, preferences don't necessarily have a precise numerical value.

Deployment of planning systems[edit]

See also[edit]

Lists

References[edit]

  1. ^ Ghallab, Malik; Nau, Dana S.; Traverso, Paolo (2004), Automated Planning: Theory and Practice, Morgan Kaufmann, ISBN 1-55860-856-7 

Further reading[edit]

External links[edit]

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