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 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

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”

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

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

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

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

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

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

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

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

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