In general we will favor shorter plans over longer plans. In order to prevent the revisiting intermediate states, we keep track of the visited intermediate states.
The reason for this is that Prolog would happily move a to b, and back again ad infinitum. With the above considerations it is not garantueed that Prolog can find a solution. In that case we perform any legal action to an intermediate state and try to reach the `Final` state from there. The `strips/3` clause does the heavy lifting of coming up with a plan. Solve(Initial, Final, Plan) :- strips(Initial, Final, Plan). We delegate the actual solving to our implementation of STRIPS. We will introduce a top-level term that allows us to come up with a plan to go from an `Initial` state to a `Final` state. "p", "q", "r".Īs an example the state above is represented as "a", "b", "c", and `Y` can either be a block or a place on the table, e.g. We will represent a state in the blocks world as a list of terms `on(X, Y)`. Our world has three blocks: a, b, c, and three places on the table: p, q, r. Moreover, some kinds of blocks cannot have other blocks stacked on top of them. Because of this, any blocks that are, at a given time, under another block cannot be moved. Only one block may be moved at a time: it may either be placed on the table or placed atop another block. The goal is to build one or more vertical stacks of blocks. A set of wooden blocks of various shapes and colors sitting on a table. > one of the most famous planning domains in artificial intelligence. > an automated planner developed by Richard Fikes and Nils Nilsson B, C) must start in upper case and that the sentence has to be simple, meaning that the program is not able to process for instance a sentence like “the block B is on the table, while the block C is red and is on the block B”.This notebook explores () Stanford Research Institute Problem Solver. The only constraint is that proper nouns (e.g. The program is able to process lots of sentences, provided that they are correct in the domain of interest. Once the Planner has performed the needed actions, the user is informed of the new situation. Instead when a command is requested by the user, it is transformed into a goal to reach and the execution of the Planner is required through PySWIP in order to reach this goal. When an assertion is entered by the user, the program uses PySWIP in order to add new identified facts to the SWI-Prolog knowledge base, while when a query is entered, always using PySWIP the program forwards the correct equivalent query to SWI-Prolog interpreter, so it can answer correctly according to the current knowledge base. The use of this library was necessary due to the decision of use Prolog in order to implement the Planner Algorithm. All those actions have been implemented using another Python library, namely PySWIP, a Python - SWI-Prolog bridge enabling to query SWI-Prolog in Python programs. Instead in case of a command it has to run the planner algorithm in order to reach the required goal. In case of an assertion, the program has to add facts from it to the knowledge base, according to the Planner Algorithm, while in case of a query it has to answer it according to the knowledge base. Therefore it can act differently according to the cases. Once obtained the tagged tree structure of the entered sentence, the program explores it in order to understand what the user wants. Once the user has entered the sentence, the program takes, tokenizes and transforms it in a part-of- speech tagged tree structure. This library allows to build Python programs which work with human language data, providing text processing for classification, tokenization, tagging and parsing. Indeed the program uses a Python library for NLP, namely Natural Language Toolkit (NLTK). In order to recognize the type of sentence the program uses the Natural Language Processing (NLP), a branch of AI that helps computers understand, interpret and manipulate human language. Once entered a sentence, the program recognize what kind it is and react accordingly to the result. Using the program the user has only to type sentences (reasonables in the domain of interest) that can be assertions, queries or commands. The goal of this project was to implement a program that allows users to solve the blocks world problem interacting only using the natural language.
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