#Prolog program for block world problem

[mainswhite]

Prolog program for block world problem

Prolog program for block world problem code#

Prolog program for block world problem plus#

% You have to remember the path taken so far to avoid cycles, % Finding a path from an Initial State I to a Final State F. % Moving blocks between three stacks, also recording the move % (The path is given in the reverse order no matter)Ītomic_list_concat(StateStrs," ~w (~q)",).ĭebug(pather,".Accepted: ~w",).ĭebug(pather,".Visited: ~w",).ĭebug(pather,".Already moved: ~w",).ĭebug(pather,"Final state reached: ~w",).ĭebug(pather,"Now at: ~w with path length ~d and path ~w",). As Prolog doesn't allow to switch out the search algorithm (why not? it's been over 40 years), you have to roll one yourself.Ītomic_list_concat("],S). You really need breadth-first search (or rather, Iterative Deepening).

Prolog program for block world problem code#

In the code below, the check is done by fail_if_visited/2.Īdditionally, a depth-first search according to the above will find a solution path, but the will likely not be a short path and not the solution sought. actually a nice exercise select a move/2 probabilistically. Otherwise the program may cycle forever (depending on how it hits the move/2 move-generating predicate. You have to check on each state expansion whether a new state might already be on the path. In fact, you construct the path through state path on return only in the third argument here: path(X,Y,).

Prolog program for block world problem plus#

(Additionally, the solution you give is not a reachable state, the second line has a void on a wrong position, plus the path is reversed).

Failure to test whether a state has already been visited.

Depth first search through state space.

Other improvements: Use a list to represent the stack, this makes the state much more appealing.ġ That's the year Prolog was discovered/conceived/delivered. Rather stick to your highly clean style and rather do more querying instead! That is what Prolog is excellent at! Yes, I could have improved it somewhat, say by using path/4, but not by much. Note that I did not dare to touch your clean code. OK, one heuristics would be the term size of a solution. You would need to inform the program what kind of preference you have. So if we are exploring this set, where shall we start? What does it mean to be the best solution? The one that when uploaded on Utube produces the most upvotes? So what we are doing here is completely uninformed search. Or, see it from another angle: The set of solutions for X is pretty large. These are all fundamental questions, but they are independent of the programming problem as such. Shouldn't it be finite? And: Is it possible to rule out such long paths for such trivial problems? Any toddler can keep the search space small. How far can you get with this formulation? Currently, I found a path of length 48. Oh yes, there are! Now it does make sense to rule out such paths. Are there really any cycles? Let's ask that first:Į = state(void,on(c,void),on(a,on(b,void))) Now, is this really the best way to ask? After all, many of the answers might be simple cycles, putting a block into one place and back again. And out of a sudden Prolog answered with a shorter answer than you expected! See what I did? I only added length(X, N) in front. Path( state(on(c,on(b,on(a,void))), void, void), OK, so let's try out any length! Also this is something Prolog loves. How long shall that list be? We know not (that is, I don't). Instead of asking for a concrete answer which demands from your Prolog system quite a bit of ingenuity, let's formulate your answer as a query! I tried it and realized that you have some nasty syntax errors in them, and after that, the query failed.īut there is a cheaper way! Let's just limit the length of the list and let Prolog fill out the rest. Instead, you can reformulate your queries sparingly. State(on(c,on(b,on(a,void))), void, void)].įor a first test, there is no need to rewrite your code. State(void, on(c,void), on(void(a,on(b,void))), I got ERROR: Out of local stack, but I want that that X would be X=[state(void, void, on(c,on(a,on(b,void)))), The problem is that the predicate path doesn't work as I want, i.e., if I type path(state(on(c,on(b,on(a,void))), void, void), state(void, void, on(c,on(a,on(b,void)))), X). Where move give us the possible movements that you can use and path should give us the path that you have to take from X to Y. Move(state(X, OldY, on(Z, NewZ)), state(X, on(Z, OldY), NewZ)). Move(state(OldX, Y, on(Z, NewZ)), state(on(Z, OldX), Y, NewZ)). Move(state(X, on(Y, NewY), OldZ), state(X, NewY, on(Y, OldZ))). Move(state(OldX, on(Y, NewY), Z), state(on(Y, OldX), NewY, Z)). Move(state(on(X, NewX), Y, OldZ), state(NewX, Y, on(X, OldZ))). I have the following code: move(state(on(X, NewX), OldY, Z), state(NewX, on(X, OldY), Z)).

[pinerhr]

Prolog program for block world problem

#Prolog program for block world problem driver

#Prolog program for block world problem driver

bus_scheduling_csplib.lp: Bus driver scheduling (CSPLib problem #22).bus_scheduling.lp4: Scheduling the number of buses for 6 days (from Taha "Operations Research").bus_scheduling.lp: Scheduling the number of buses for 6 days (from Taha "Operations Research").building_blocks2.lp4: Building Blocks puzzle (Dell Logic Puzzles), faster version.building_blocks2.lp: Building Blocks puzzle (Dell Logic Puzzles), faster version.building_blocks.lp: Building Blocks puzzle (Dell Logic Puzzles).averbach_1.4.lp: Seating problem, example 1.4 in Averbach & Chein "Problem Solving Through Recreational Mathematics".assignment.lp4: Assignment problem (from Winston "Operations Research").assignment.lp: Assignment problem (from Winston "Operations Research").arch_friends.lp4: Arch friends puzzle (Dells Logic Puzzles).arch_friends.lp: Arch friends puzzle (Dells Logic Puzzles).alldifferent_except_0.lp4: Alldifferent except 0.alldifferent_except_0.lp: Alldifferent except 0.all_interval.lp4: All interval problem (CSPLib problem #7).all_interval.lp: All interval problem (CSPLib problem #7).a_round_of_golf.lp4: A round of gold (Dell Logic Puzzles).a_round_of_golf.lp: A round of gold (Dell Logic Puzzles).3_jugs.lp4: 3 jugs problem (as a graph problem).3_jugs.lp: 3 jugs problem (as a graph problem).The encodings ported to Gringo 4 has extension. See Common constraint programming problems for a list of different implementations.Īll are written in Gringo/Clingo/clasp, but should be quite easy to convert to other ASP systems, e.g. Almost all of of them have also been done in some Constraint Programming system, for example MiniZinc. Also see Potasssco Labs, and Potassco Wiki Potassco Answer Set Solving Collection, includes clasp, Gringo, Clingo, and other tools.Teaching Answer Set Programming (Potassco teaching material for Answer Set Programming).Collection on Answer Set Programming (ASP) and more (Torsten Schaub).It's quite theoretical but also contains a lot of encoding examples. The book Knowledge Representation, Reasoning and Declarative Problem Solving by Chitta Baral (2003, Cambridge University Press, ISBN: 9780521147750).Thomas Eiter, Giovambattista Ianni, Thomas Krennwallner: Answer Set Programming: A Primer (PDF).Texas Action Group (TAG, TAG Members).Third Answer Set Programming Competition - 2011.The Second Answer Set Programming Competition.The First Answer Set Programming System Competition.I have blogged about this in A first look at Answer Set Programming. The computational process employed in the design of many answer set solvers is an enhancement of the DPLL algorithm and, in principle, it always terminates (unlike Prolog query evaluation, which may lead to an infinite loop). In ASP, search problems are reduced to computing stable models, and answer set solvers - programs for generating stable models are used to perform search. It is based on the stable model (answer set) semantics of logic programming. compiling D:/TP Prolog/Sample_Codes/ for byte code.D:/TP Prolog/Sample_Codes/ compiled, 8 lines read - 1409 bytes written, 15 msyes| ?- move(4,source,target,auxiliary).My Answer Set Programming Page My Answer Set Programming PageĪnswer set programming (ASP) is a form of declarative programming oriented towards difficult (primarily NP-hard) search problems. Here N number of disks will have to be shifted from Source peg to Target peg keeping Auxiliary peg as intermediate.įor example – move(3, source, target, auxiliary). To solve this, we have to write one procedure move(N, Source, Target, auxiliary). The following diagram depicts the starting setup for N=3 disks. There are two conditions that are to be followed while solving this problem −Ī larger disk cannot be placed on a smaller disk. Towers of Hanoi Problem is a famous puzzle to move N disks from the source peg/tower to the target peg/tower using the intermediate peg as an auxiliary holding peg.

[psychicjust]

Prolog program for block world problem

#Prolog program for block world problem code

#Prolog program for block world problem plus

compiling D:/TP Prolog/Sample_Codes/ for byte code.D:/TP Prolog/Sample_Codes/ compiled, 3 lines read - 529 bytes written, 15 msyes| ?- gt(10,100).X is smalleryes| ?- gt(150,100).X is greater or equaltrue ?yes| ?- gte(10,20).X is smaller(15 ms) yes| ?- gte(100,20).X is greatertrue ?yes| ?- gte(100,100). Stack(A, B) Execution of a plan: achieved through a data structure called Triangular Table. Program % If-Then-Else statementgt(X,Y) :- X >= Y,write('X is greater or equal').gt(X,Y) :- X Y,write('X is greater').gte(X,Y) :- X =:= Y,write('X and Y are same').gte(X,Y) :- X decision statements are If-Then-Else statements. compiling D:/TP Prolog/Sample_Codes/ for byte code.D:/TP Prolog/Sample_Codes/ compiled, 14 lines read - 1700 bytes written, 16 msyes| ?- count_down(12,17).5true ? 4true ? 3true ? 2true ? 1true ? 0yes| ?- count_up(5,12).10true ? 11true ? 12true ? 13true ? 14true ? 15true ? 16true ? 17yes| ?- Decision Making count_up(L, H) :- between(L, H, Y), Z is L + Y, write(Z), nl. Let us see an example program − count_down(L, H) :- between(L, H, Y), Z is H - Y, write(Z), nl. So, we can use the between() to simulate loops. Now create a loop that takes lowest and highest values. compiling D:/TP Prolog/Sample_Codes/ for byte code.D:/TP Prolog/Sample_Codes/ compiled, 4 lines read - 751 bytes written, 16 ms(16 ms) yes| ?- count_to_10(3).345678910true ?yes| ?. There are no direct loops in some other languages, but we can simulate loops with few different techniques. In general, for, while, do-while are loop constructs in programming languages (like Java, C, C++).Ĭode block is executed multiple times using recursive predicate logic.

#Prolog program for block world problem code

Loop statements are used to execute the code block multiple times. Machine Learning, 8: 279–292.In this chapter, we will discuss loops and decision making in Prolog. (1995) Temporal difference learning and TD-GAMMON. Journal of Logic Programming 19/20: 629–679. (1994) Inductive logic programming: Theory and methods. (1994) Inductive Logic Programming: Techniques and Applications. Journal of Artificial Intelligence Research, 4: 237–285. Kaelbling, L., Littman, M., and Moore, A. (1971) STRIPS: A new approach to the application of theorem proving. Workshop on Inductive Logic Programming, pages 133–141, Springer, Berlin.įikes, R.E., and Nilsson, N.J.

#Prolog program for block world problem plus

(Additionally, the solution you give is not a reachable state, the second line has a void on a wrong position, plus the path is reversed). Failure to test whether a state has already been visited. (1997) Using logical decision trees for clustering. a block world problem It is simply because you do two things: Depth first search through state space. on Artificial Intelligence, Morgan Kaufmann, San Mateo, CA.ĭe Raedt, L., and Blockeel, H. (1991) Input generalization in delayed reinforcement learning: An algorithm and performance comparisons. Workshop on Inductive Logic Programming, pages 77–84, Springer, Berlin.Ĭhapman, D., and Kaelbling, L. (1997) Lookahead and discretization in ILP. Wadsworth, Belmont.īlockeel, H., and De Raedt, L. (1984) Classification and Regression Trees. (1997) Lazy incremental learning of control knowledge for efficiently obtaining quality plans. (1997) Experiments with Top-down Induction of Logical Decision Trees.

[mainsforme]

Prolog program for block world problem

Prolog is highly used in artificial intelligence(AI). LISP (another logic programming language) dominates over prolog with respect to I/O features.Ģ. Makes it easier to play with any algorithm involving lists.ġ. Doesn’t need a lot of programming effort.Ģ. The above fact, so output was 'Yes', otherwiseĮxplanation : As our knowledge base does notĬontain the above fact, so output was 'No'.ġ. Recursion : Recursion is the basis for any search in program.Įxplanation : As our knowledge base contains Backtracking : When a task fails, prolog traces backwards and tries to satisfy previous task.ģ. Unification : The basic idea is, can the given terms be made to represent the same structure.Ģ. So, a typical prolog fact goes as follows :įormat : relation(entity1, entity2. Their relation is expressed at the start and outside the parenthesis. Entities are written within the parenthesis separated by comma (, ). Facts contain entities and their relation. Prolog facts are expressed in definite pattern. So, Knowledge Base can be considered similar to database, against which we can query. We get output as affirmative if our query is already in the knowledge Base or it is implied by Knowledge Base, otherwise we get output as negative. These facts constitute the Knowledge Base of the system. Formulation or Computation is carried out by running a query over these relations. Core heart of prolog lies at the logic being applied. In prolog, logic is expressed as relations (called as Facts and Rules). Unlike many other programming languages, Prolog is intended primarily as a declarative programming language. It has important role in artificial intelligence. Differences between Procedural and Object Oriented Programming.Arrow operator -> in C/C++ with Examples.Modulo Operator (%) in C/C++ with Examples.Inorder Tree Traversal without Recursion.Tree Traversals (Inorder, Preorder and Postorder).Breadth First Search or BFS for a Graph.Unique paths covering every non-obstacle block exactly once in a grid.Print all possible paths from top left to bottom right of a mXn matrix.Count all possible paths from top left to bottom right of a mXn matrix.Count number of ways to reach destination in a Maze.The Knight’s tour problem | Backtracking-1.Warnsdorff’s algorithm for Knight’s tour problem.Printing all solutions in N-Queen Problem.Difference between Informed and Uninformed Search in AI.Understanding PEAS in Artificial Intelligence.Uniform-Cost Search (Dijkstra for large Graphs).Introduction to Hill Climbing | Artificial Intelligence.ISRO CS Syllabus for Scientist/Engineer Exam.ISRO CS Original Papers and Official Keys.GATE CS Original Papers and Official Keys.

[ratusalim]

Early milestones in AI

The first AI programs

The earliest successful AI program was written in 1951 by Christopher Strachey, later director of the Programming Research Group at the University of Oxford. Strachey’s checkers (draughts) program ran on the Ferranti Mark I computer at the University of Manchester, England. By the summer of 1952 this program could play a complete game of checkers at a reasonable speed.

Information about the earliest successful demonstration of machine learning was published in 1952. Shopper, written by Anthony Oettinger at the University of Cambridge, ran on the EDSAC computer. Shopper’s simulated world was a mall of eight shops. When instructed to purchase an item, Shopper would search for it, visiting shops at random until the item was found. While searching, Shopper would memorize a few of the items stocked in each shop visited (just as a human shopper might). The next time Shopper was sent out for the same item, or for some other item that it had already located, it would go to the right shop straight away. This simple form of learning, as is pointed out in the introductory section What is intelligence?, is called rote learning.

The first AI program to run in the United States also was a checkers program, written in 1952 by Arthur Samuel for the prototype of the IBM 701. Samuel took over the essentials of Strachey’s checkers program and over a period of years considerably extended it. In 1955 he added features that enabled the program to learn from experience. Samuel included mechanisms for both rote learning and generalization, enhancements that eventually led to his program’s winning one game against a former Connecticut checkers champion in 1962.

Evolutionary computing

Samuel’s checkers program was also notable for being one of the first efforts at evolutionary computing. (His program “evolved” by pitting a modified copy against the current best version of his program, with the winner becoming the new standard.) Evolutionary computing typically involves the use of some automatic method of generating and evaluating successive “generations” of a program, until a highly proficient solution evolves.

A leading proponent of evolutionary computing, John Holland, also wrote test software for the prototype of the IBM 701 computer. In particular, he helped design a neural-network “virtual” rat that could be trained to navigate through a maze. This work convinced Holland of the efficacy of the bottom-up approach. While continuing to consult for IBM, Holland moved to the University of Michigan in 1952 to pursue a doctorate in mathematics. He soon switched, however, to a new interdisciplinary program in computers and information processing (later known as communications science) created by Arthur Burks, one of the builders of ENIAC and its successor EDVAC. In his 1959 dissertation, for most likely the world’s first computer science Ph.D., Holland proposed a new type of computer—a multiprocessor computer—that would assign each artificial neuron in a network to a separate processor. (In 1985 Daniel Hillis solved the engineering difficulties to build the first such computer, the 65,536-processor Thinking Machines Corporation supercomputer.)

Holland joined the faculty at Michigan after graduation and over the next four decades directed much of the research into methods of automating evolutionary computing, a process now known by the term genetic algorithms. Systems implemented in Holland’s laboratory included a chess program, models of single-cell biological organisms, and a classifier system for controlling a simulated gas-pipeline network. Genetic algorithms are no longer restricted to “academic” demonstrations, however; in one important practical application, a genetic algorithm cooperates with a witness to a crime in order to generate a portrait of the criminal.

Logical reasoning and problem solving

The ability to reason logically is an important aspect of intelligence and has always been a major focus of AI research. An important landmark in this area was a theorem-proving program written in 1955–56 by Allen Newell and J. Clifford Shaw of the RAND Corporation and Herbert Simon of the Carnegie Mellon University. The Logic Theorist, as the program became known, was designed to prove theorems from Principia Mathematica (1910–13), a three-volume work by the British philosopher-mathematicians Alfred North Whitehead and Bertrand Russell. In one instance, a proof devised by the program was more elegant than the proof given in the books.

Newell, Simon, and Shaw went on to write a more powerful program, the General Problem Solver, or GPS. The first version of GPS ran in 1957, and work continued on the project for about a decade. GPS could solve an impressive variety of puzzles using a trial and error approach. However, one criticism of GPS, and similar programs that lack any learning capability, is that the program’s intelligence is entirely secondhand, coming from whatever information the programmer explicitly includes.

English dialogue

Two of the best-known early AI programs, Eliza and Parry, gave an eerie semblance of intelligent conversation. (Details of both were first published in 1966.) Eliza, written by Joseph Weizenbaum of MIT’s AI Laboratory, simulated a human therapist. Parry, written by Stanford University psychiatrist Kenneth Colby, simulated a human paranoiac. Psychiatrists who were asked to decide whether they were communicating with Parry or a human paranoiac were often unable to tell. Nevertheless, neither Parry nor Eliza could reasonably be described as intelligent. Parry’s contributions to the conversation were canned—constructed in advance by the programmer and stored away in the computer’s memory. Eliza, too, relied on canned sentences and simple programming tricks.

AI programming languages

In the course of their work on the Logic Theorist and GPS, Newell, Simon, and Shaw developed their Information Processing Language (IPL), a computer language tailored for AI programming. At the heart of IPL was a highly flexible data structure that they called a list. A list is simply an ordered sequence of items of data. Some or all of the items in a list may themselves be lists. This scheme leads to richly branching structures.

In 1960 John McCarthy combined elements of IPL with the lambda calculus (a formal mathematical-logical system) to produce the programming language LISP (List Processor), which remains the principal language for AI work in the United States. (The lambda calculus itself was invented in 1936 by the Princeton logician Alonzo Church while he was investigating the abstract Entscheidungsproblem, or “decision problem,” for predicate logic—the same problem that Turing had been attacking when he invented the universal Turing machine.)

The logic programming language PROLOG (Programmation en Logique) was conceived by Alain Colmerauer at the University of Aix-Marseille, France, where the language was first implemented in 1973. PROLOG was further developed by the logician Robert Kowalski, a member of the AI group at the University of Edinburgh. This language makes use of a powerful theorem-proving technique known as resolution, invented in 1963 at the U.S. Atomic Energy Commission’s Argonne National Laboratory in Illinois by the British logician Alan Robinson. PROLOG can determine whether or not a given statement follows logically from other given statements. For example, given the statements “All logicians are rational” and “Robinson is a logician,” a PROLOG program responds in the affirmative to the query “Robinson is rational?” PROLOG is widely used for AI work, especially in Europe and Japan.

Researchers at the Institute for New Generation Computer Technology in Tokyo have used PROLOG as the basis for sophisticated logic programming languages. Known as fifth-generation languages, these are in use on nonnumerical parallel computers developed at the Institute.

Other recent work includes the development of languages for reasoning about time-dependent data such as “the account was paid yesterday.” These languages are based on tense logic, which permits statements to be located in the flow of time. (Tense logic was invented in 1953 by the philosopher Arthur Prior at the University of Canterbury, Christchurch, New Zealand.)

Microworld programs

To cope with the bewildering complexity of the real world, scientists often ignore less relevant details; for instance, physicists often ignore friction and elasticity in their models. In 1970 Marvin Minsky and Seymour Papert of the MIT AI Laboratory proposed that likewise AI research should focus on developing programs capable of intelligent behaviour in simpler artificial environments known as microworlds. Much research has focused on the so-called blocks world, which consists of coloured blocks of various shapes and sizes arrayed on a flat surface.

An early success of the microworld approach was SHRDLU, written by Terry Winograd of MIT. (Details of the program were published in 1972.) SHRDLU controlled a robot arm that operated above a flat surface strewn with play blocks. Both the arm and the blocks were virtual. SHRDLU would respond to commands typed in natural English, such as “Will you please stack up both of the red blocks and either a green cube or a pyramid.” The program could also answer questions about its own actions.Although SHRDLU was initially hailed as a major breakthrough, Winograd soon announced that the program was, in fact, a dead end. The techniques pioneered in the program proved unsuitable for application in wider, more interesting worlds. Moreover, the appearance that SHRDLU gave of understanding the blocks microworld, and English statements concerning it, was in fact an illusion. SHRDLU had no idea what a green block was.

Another product of the microworld approach was Shakey, a mobile robot developed at the Stanford Research Institute by Bertram Raphael, Nils Nilsson, and others during the period 1968–72. The robot occupied a specially built microworld consisting of walls, doorways, and a few simply shaped wooden blocks. Each wall had a carefully painted baseboard to enable the robot to “see” where the wall met the floor (a simplification of reality that is typical of the microworld approach). Shakey had about a dozen basic abilities, such as TURN, PUSH, and CLIMB-RAMP.

Critics pointed out the highly simplified nature of Shakey’s environment and emphasized that, despite these simplifications, Shakey operated excruciatingly slowly; a series of actions that a human could plan out and execute in minutes took Shakey days.

The greatest success of the microworld approach is a type of program known as an expert system, described in the next section.