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Universal Plans for Reactive Robots in Unpredictable Environments

Universal Plans for Reactive Robots in Unpredictable Environments. By M.J. Schoppers Presented by: Javier Martinez. Overview. Integrate goal-directed advanced planning with sensor-driven reaction Allow the planner to generate new plans automatically. Motivation.

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Universal Plans for Reactive Robots in Unpredictable Environments

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  1. Universal Plans for Reactive Robots in Unpredictable Environments By M.J. Schoppers Presented by: Javier Martinez

  2. Overview • Integrate goal-directed advanced planning with sensor-driven reaction • Allow the planner to generate new plans automatically

  3. Motivation • The linear approach used traditionally in AI has certain drawbacks such as: • Requires a lot of a-priori information • Time consuming • Delays actions arrival • Additionally, the plans it produces cannot cope with unpredictable environments

  4. Ideas • Not committing to any particular sequence of events • Let the environment dictate what to do next • “Planning is the goal-directed selection of reactions to possible situations” • “If a situation satisfying condition P arises while trying to achieve goal G, then the appropriate response is action A”

  5. Planner Elements • Primitive Actions: are I/O conditions (in the context of a robot) that are maintained for an unspecified amount of time (i.e. speed up or slow down) • Action Descriptions: is the actual motions that comprise them • Domain Constraints: restrictions particular to a domain

  6. Planner Elements • The Goal: is a condition to be achieved, instead of a world state

  7. Universal plans • Interpretation: The plan has the shape of a tree and the interpreter traverses it by evaluating the environment at each node • Hierarchy: The idea is that a plan can become part of another as an action actions, thus being a sub-plan • Competence: Actions cannot replace planning even when both fulfill the same goal as plans are general while actions are conditioned

  8. Plan Synthesis • The process is done by back-chaining from the goal • When back-chaining, what was a precondition now becomes a goal but in a negated form • Back-chaining terminates when the preconditions are met or when a contradiction is found

  9. Related Work • Procedural Reasoning System (PRS): • Reduced the amount of planning • Behaviors are decomposed by hand • Suffered from rigidity by not dealing with goal selection and rejection • REX Project: • Continuously evaluates predicates • Lacks symbolic representation • Plans are hand-coded

  10. Related Work • Triangle Tables: • Create an index to a set of operators by extracting data from three sets: • Were the first ones to use the environment in the planning stage • It suffers from the same rigidity as PRS

  11. Advantages & Limitations • Being the plan basically a tree we can expect a computational efficiency of O(log(n)) • Approach limited to state spaces • The approach is dependant on how fast sensors can deliver information to the plan interpreter

  12. Paper Comments • Goal directed planning along with reaction based behavior seem a more natural way of achieving goals • Examples were difficult to follow and assumes too much knowledge about STRIPS operators

  13. Thank you!

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