Intelligence Semantics

Planning and Learning by Analogical Reasoning by Manuela M. Veloso

By Manuela M. Veloso

This learn monograph describes the mixing of analogical and case-based reasoning into normal challenge fixing and making plans as a mode of speedup studying. the strategy, in line with derivational analogy, has been totally carried out in PRODIGY/ANALOGY and confirmed in perform to be amenable to scaling up, either by way of area and challenge complexity.
In this paintings, the strategy-level studying approach is forged for the 1st time because the automation of the full cycle of building, storing, retrieving, and flexibly reusing challenge fixing adventure. The algorithms concerned are awarded intimately and various examples are given. therefore the publication addresses researchers in addition to practitioners.

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10). At step 8 of the third and last search path explored the application of the operator (MDVE-ROCKET) is postponed and the search terminates successfully. To illustrate the formalization, we consider two points in the search procedure and instantiate the concepts introduced. 48 C H A P T E R 3. THE PROBLEM SOLVER ( n l r u n - p r o b ' r o c k e t - 2 o b j s ) 11. tnl2 ( l o a d - r o c k e t o b j l locA) S o l v i n g the problem r o c k e t - 2 o b j s : Initial state : ((at objl locA) (at ebj2 1etA) (at rocket iocA)) Goal statement: (and (at objl locB) (at obj2 locB)) 12.

We say that this operator is relevant to the given goal. If the preconditions of the chosen operator are true, the operator can be applied. , new goals to be achieved. The cycle repeats until all the conjuncts from the goal expression are true in the world. NOLIMIT proceeds in this apparently simple way. Its nonlinear character stems from working with a set of goals in this cycle, as opposed to the top goal in a goal stack. Dynamic goal selection enables NOLIMIT to interleave CHAPTER 3. THE PROBLEM SOLVER 40 plans, exploiting c o m m o n subgoals and addressing issues of resource contention.

L n s t a n t i a t e d _ O p e r a t o r s (act, T, 70) identifies the operators (or/and inference rules) that have an effect that unifies with the goal at the active goal node, act. These operators are the relevant operators to the goal. This means that if when applied their effects are such that the goal 3These facts are declarative and their procedural meaning is described by the problem solving algorithms presented next. 3. ; Output : An expanded search tree T' = (N',E'), new setS c4N,,~N,,S N, Of the new active, failed, and suspended search tree nodes, and a new active leaf node.

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