Program move(state(middle,onbox,middle,hasnot), grasp, state(middle,onbox,middle,has)).move(state(P,onfloor,P,H), climb, state(P,onbox,P,H)).move(state(P1,onfloor,P1,H), drag(P1,P2), state(P2,onfloor,P2,H)).move(state(P1,onfloor,B,H), walk(P1,P2), state(P2,onfloor,B,H)).canget(state(_,_,_,has)).canget(State1) :- move(State1,_,State2), canget(State2). When we have reached to the state ‘ has>’, this indicates ‘ has banana’. Here we pass a state, so this will perform move predicate from one state to another using different actions, then perform canget() on state 2. Monkey can move from one place to another using walk or move clauses.Īnother predicate will be canget(). Lecture 15- Robotic Knowledge Representation and Inferencing Prolog.
ASP solvers normally ground the input program and then use a SAT solver to find stable. The push or drag operation moves the block from one place to another. Traditionally, ASP notably targets difficult search problems. on top state), by performing the action climb. has not state), then using the grasp action, it will change from has not state to have state.įrom the floor, it can move to the top of the block (i.e. When the block is at the middle, and monkey is on top of the block, and monkey does not have the banana (i.e. We have some predicates that will move from one state to another state, by performing action.
We will create some predicates as follows − Now, let us see how we can solve this using Prolog. Winograds blocks-world problem solver was written in Microplanner, a language which we now recognize as a sort of Prolog. When the monkey is on the block, and block is at the center, then the monkey can get the bananas. So the vertical position of the monkey will be changed. If monkey and the block both are on the floor, and block is at the center, then the monkey can climb up on the block. If the block position is not at the center, then monkey can drag it to the center. From the above image, we can see that both the monkey and the block are on the floor. Monkey can reach the block, if both of them are at the same level. Below are few observations in this case − So if the monkey is clever enough, he can come to the block, drag the block to the center, climb on it, and get the banana. The monkey wants the banana, but cannot reach it. Not since summer 1972 1.Instead, you can reformulate your queries sparingly. There is a block (or chair) present in the room near the window. For a first test, there is no need to rewrite your code. Problem StatementĪ hungry monkey is in a room, and he is near the door.īananas have been hung from the center of the ceiling of the room. to construct a plan for initial state and goal. Moreover, we demonstrate that, using monotonicity and anti-monotonicity of modules, one can significantly reduce the search space of a solution to a modular system.In this prolog example, we will see one very interesting and famous problem, The Monkey and Banana Problem. Planning is a particular type of problem solving in which actions. We prove that, even with individual modules being polytime solvable, the framework is expressive enough to capture all of NP, a property which does not hold without loop. We study the expressive power of our framework and demonstrate that adding the feedback operator increases the expressive power considerably. In particular, we use a model-theoretic setting and introduce a feedback (loop) operator on modules. We start our development from a previous work,, but modify and extend that framework significantly. We develop a modular framework where parts of a modular system can be written in different languages. Motivated by the need to combine systems and logics, we develop a modular approach to the model expansion (MX) problem, a task which is common in applications such as planning, scheduling, computational biology, formal verification.
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