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796:"domain independent" to emphasize 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.
977:. The Simple Temporal Network with Uncertainty (STNU) is a scheduling problem which involves controllable actions, uncertain events and temporal constraints. Dynamic Controllability for such problems is a type of scheduling which requires a temporal planning strategy to activate controllable actions reactively as uncertain events are observed so that all constraints are guaranteed to be satisfied.
1095:-complete. A particular case of contiguous planning is represented by FOND problems - for "fully-observable and non-deterministic". If the goal is specified in LTLf (linear time logic on finite trace) then the problem is always EXPTIME-complete and 2EXPTIME-complete if the goal is specified with LDLf.
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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
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because each step of the plan is represented by a set of states rather than a single perfectly observable state, as in the case of classical planning. The selected actions depend on the state of the system. For example, if it rains, the agent chooses to take the umbrella, and if it doesn't, they may
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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, in contrast to those in which there is no input domain specified. Such planners are called
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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
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Conformant planning is when the agent is uncertain about the state of the system, and it cannot make any observations. The agent then has beliefs about the real world, but cannot verify them with sensing actions, for instance. These problems are solved by techniques similar to those of classical
614:
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
886:, 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.
1104:
planning, but where the state space is exponential in the size of the problem, because of the uncertainty about the current state. A solution for a conformant planning problem is a sequence of actions. Haslum and
Jonsson have demonstrated that the problem of conformant planning is
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We speak of "contingent planning" when the environment is observable through sensors, which can be faulty. It is thus a situation where the planning agent acts under incomplete information. For a contingent planning problem, a plan is no longer a sequence of actions but a
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which are unknown for the planner. The planner generates two choices in advance. For example, if an object was detected, then action A is executed, if an object is missing, then action B is executed. A major advantage of conditional planning is the ability to handle
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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
1003:, 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.
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An early example of a conditional planner is “Warplan-C” which was introduced in the mid 1970s. What is the difference between a normal sequence and a complicated plan, which contains if-then-statements? It has to do with uncertainty at
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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.
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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?
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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.
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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.
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planning system, which is a hierarchical planner. Action names are ordered in a sequence and this is a plan for the robot. Hierarchical planning can be compared with an automatic generated
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1037:. The disadvantage is, that a normal behavior tree is not so expressive like a computer program. That means, the notation of a behavior graph contains action commands, but no
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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
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1299:
Neufeld, Xenija and
Mostaghim, Sanaz and Sancho-Pradel, Dario and Brand, Sandy (2017). "Building a Planner: A Survey of Planning Systems Used in Commercial Video Games".
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1021:. A difference to the more common reward-based planning, for example corresponding to MDPs, preferences don't necessarily have a precise numerical value.
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or if-then-statements. Conditional planning overcomes the bottleneck and introduces an elaborated notation which is similar to a
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1108:-complete, and 2EXPTIME-complete when the initial situation is uncertain, and there is non-determinism in the actions outcomes.
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problems, the solutions are complex and must be discovered and optimized in multidimensional space. Planning is also related to
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Vidal, Thierry (January 1999). "Handling contingency in temporal constraint networks: from consistency to controllabilities".
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Further, plans can be defined as sequences of actions, because it is always known in advance which actions will be needed.
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In preference-based planning, the objective is not only to produce a plan but also to satisfy user-specified
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often needs to be revised online. Models and policies must be adapted. Solutions usually resort to iterative
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1427:(Technical report). Technical Report TR-2008-936, Department of Computer Science, University of Rochester.
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that is exponential in the set, planning, similarly to many other computational problems, suffers from the
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The most commonly used languages for representing planning domains and specific planning problems, such as
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Can the current state be observed unambiguously? There can be full observability and partial observability.
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1455:. International Joint Conference of Artificial Intelligence (IJCAI). Pasadena, CA: AAAI. Archived from
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A survey of planning in intelligent agents: from externally motivated to internally motivated systems
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1656:. Lecture Notes in Computer Science. Vol. 1809. Springer Berlin Heidelberg. pp. 308–318.
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The simplest possible planning problem, known as the
Classical Planning Problem, is determined by:
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discrete or continuous? If they are discrete, do they have only a finite number of possible values?
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Michael L. Littman showed in 1998 that with branching actions, the planning problem becomes
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1483:. Fourteenth National Conference on Artificial Intelligence. MIT Press. pp. 748–754.
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When full observability is replaced by partial observability, planning corresponds to a
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Sanelli, Valerio and
Cashmore, Michael and Magazzeni, Daniele and Iocchi, Luca (2017).
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1630:. Twenty-First International Conference on Automated Planning and Scheduling (ICAPS).
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Can several actions be taken concurrently, or is only one action possible at a time?
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Effective heuristics and belief tracking for planning with incomplete information
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1074:. This helps to reduce the state space and solves much more complex problems.
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Is the objective of a plan to reach a designated goal state, or to maximize a
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Short-term human robot interaction through conditional planning and execution
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problems when uncertainty is involved and can also be understood in terms of
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Automata-Theoretic
Foundations of FOND Planning for LTLf and LDLf Goals
1379:. Artificial Intelligence Planning Systems. Elsevier. pp. 189–197.
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834: in this section. Unsourced material may be challenged and removed.
767: in this section. Unsourced material may be challenged and removed.
586: in this section. Unsourced material may be challenged and removed.
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Some
Results on the Complexity of Planning with Incomplete Information
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538:. Languages used to describe planning and scheduling are often called
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Probabilistic
Propositional Planning: Representations and Complexity
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Probabilistic planning can be solved with iterative methods such as
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An alternative language for describing planning problems is that of
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Journal of
Experimental & Theoretical Artificial Intelligence
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search, possibly enhanced by the use of state constraints (see
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How many initial states are there, finite or arbitrarily many?
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International
Conference on Automated Planning and Scheduling
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International Conference on Automated Planning and Scheduling
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Albore, Alexandre; RamĂrez, Miquel; Geffner, Hector (2011).
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Alexandre Albore; Hector Palacios; Hector Geffner (2009).
1517:. Int. Conf. Automated Planning and Scheduling. AAAI.
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Ghallab, Malik; Nau, Dana S.; Traverso, Paolo (2004),
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desired goals (such a state is called a goal state).
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A Translation-Based Approach to Contingent Planning
1407:Conditional progressive planning under uncertainty
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1511:Complexity of Planning with Partial Observability
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1061:of a plan. The idea is that a plan can react to
487:or action sequences, typically for execution by
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1029:Deterministic planning was introduced with the
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991:Partially observable Markov decision process
716:partially observable Markov decision process
701:nondeterministic actions with probabilities,
1576:Journal of Artificial Intelligence Research
1542:De Giacomo, Giuseppe; Rubin, Sasha (2018).
1676:conference: Recent Advances in AI Planning
1570:Palacios, Hector; Geffner, Hector (2009).
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721:If there are more than one agent, we have
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850:Learn how and when to remove this message
783:Learn how and when to remove this message
602:Learn how and when to remove this message
80:Learn how and when to remove this message
1370:Peot, Mark A and Smith, David E (1992).
1192:List of constraint programming languages
43:This article includes a list of general
1652:Haslum, Patrik; Jonsson, Peter (2000).
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1224:Automated Planning: Theory and Practice
1150:Applications of artificial intelligence
1125:and a long-term planning system called
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673:which can be taken only one at a time,
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832:adding citations to reliable sources
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765:adding citations to reliable sources
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584:adding citations to reliable sources
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800:Planning domain modelling languages
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1202:Outline of artificial intelligence
707:maximization of a reward function,
694:(MDP) are planning problems with:
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49:it lacks sufficient corresponding
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1731:Automated planning and scheduling
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483:that concerns the realization of
473:Automated planning and scheduling
27:Branch of artificial intelligence
1121:uses a short-term system called
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1338:from the original on 2019-08-16
1245:from the original on 2009-08-24
1160:Constraint satisfaction problem
819:needs additional citations for
752:needs additional citations for
571:needs additional citations for
128:Artificial general intelligence
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1373:Conditional nonlinear planning
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1112:Deployment of planning systems
725:, which is closely related to
475:, sometimes denoted as simply
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1197:List of emerging technologies
664:a unique known initial state,
1477:Littman, Michael L. (1997).
946:propositional satisfiability
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1140:Action description language
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939:Reduction to other problems
733:Domain independent planning
643:Do actions have a duration?
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522:processes commonly seen in
163:Natural language processing
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1410:. IJCAI. pp. 431–438.
1301:IEEE Transactions on Games
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884:hierarchical task networks
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536:combinatorial optimization
216:Hybrid intelligent systems
138:Recursive self-improvement
1694:"Planning and Scheduling"
1013:Preference-based planning
1007:Preference-based planning
906:, possibly enhanced with
692:Markov decision processes
1421:Liu, Daphne Hao (2008).
1049:. It is very similar to
340:Artificial consciousness
1508:Jussi Rintanen (2004).
1404:Karlsson, Lars (2001).
1286:10.1080/095281399146607
1088:choose not to take it.
987:Markov decision process
890:Algorithms for planning
877:combinatorial explosion
873:curse of dimensionality
524:artificial intelligence
481:artificial intelligence
211:Evolutionary algorithms
101:Artificial intelligence
64:more precise citations.
1175:Strategy (game theory)
1170:Scheduling (computing)
1119:Hubble Space Telescope
981:Probabilistic planning
927:partial-order planning
670:deterministic actions,
532:reinforcement learning
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698:durationless actions,
667:durationless actions,
550:Further information:
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1078:Contingency planning
1025:Conditional planning
828:improve this article
761:improve this article
723:multi-agent planning
580:improve this article
153:General game playing
1662:10.1007/10720246_24
1187:List of SMT solvers
1099:Conformant planning
710:and a single agent.
704:full observability,
676:and a single agent.
528:dynamic programming
499:. Unlike classical
305:Machine translation
221:Systems integration
158:Knowledge reasoning
95:Part of a series on
904:state space search
895:Classical planning
552:State space search
489:intelligent agents
113:
18:Automated planning
1599:10.1613/jair.2708
1165:Reactive planning
1051:program synthesis
964:Temporal planning
944:reduction to the
913:backward chaining
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493:autonomous robots
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133:Intelligent agent
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1019:preferences
727:game theory
477:AI planning
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121:Major goals
62:introducing
1638:2019-08-16
1610:2019-08-16
1556:2018-07-17
1528:2019-07-03
1491:2019-02-10
1463:2019-07-03
1435:2019-08-16
1342:2019-08-16
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1208:References
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246:Deepfake
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