AIMA.Core.Logic.FOL.KB.FOLKnowledgeBase.ask C# (CSharp) 메소드

        public InferenceResult ask(Sentence aQuery)
        {
            // Want to standardize apart the query to ensure
            // it does not clash with any of the sentences
            // in the database
            StandardizeApartResult saResult = _standardizeApart.standardizeApart(
                    aQuery, queryIndexical);

            // Need to map the result variables (as they are standardized apart)
            // to the original queries variables so that the caller can easily
            // understand and use the returned set of substitutions
            InferenceResult infResult = getInferenceProcedure().ask(this,
                    saResult.getStandardized());
            List<Proof> proofs = infResult.getProofs();
            foreach (Proof p in proofs)
            {
                Dictionary<Variable, Term> im = p.getAnswerBindings();
                Dictionary<Variable, Term> em = new Dictionary<Variable, Term>();
                foreach (Variable rev in saResult.getReverseSubstitution().Keys)
                {
                    em.Add((Variable)saResult.getReverseSubstitution()[rev],
                            im[rev]);
                }
                p.replaceAnswerBindings(em);
            }

            return infResult;
        }

        public void testExhaustsSearchSpace()
        {
            // Taken from AIMA pg 679
            FOLDomain domain = new FOLDomain();
            domain.addPredicate("alternate");
            domain.addPredicate("bar");
            domain.addPredicate("fri_sat");
            domain.addPredicate("hungry");
            domain.addPredicate("patrons");
            domain.addPredicate("price");
            domain.addPredicate("raining");
            domain.addPredicate("reservation");
            domain.addPredicate("type");
            domain.addPredicate("wait_estimate");
            domain.addPredicate("will_wait");
            domain.addConstant("Some");
            domain.addConstant("Full");
            domain.addConstant("French");
            domain.addConstant("Thai");
            domain.addConstant("Burger");
            domain.addConstant("$");
            domain.addConstant("_30_60");
            domain.addConstant("X0");
            FOLParser parser = new FOLParser(domain);

            // The hypothesis
            String c1 = "patrons(v,Some)";
            String c2 = "patrons(v,Full) AND (hungry(v) AND type(v,French))";
            String c3 = "patrons(v,Full) AND (hungry(v) AND (type(v,Thai) AND fri_sat(v)))";
            String c4 = "patrons(v,Full) AND (hungry(v) AND type(v,Burger))";
            String sh = "FORALL v (will_wait(v) <=> (" + c1 + " OR (" + c2
                    + " OR (" + c3 + " OR (" + c4 + ")))))";

            Sentence hypothesis = parser.parse(sh);
            Sentence desc = parser
                    .parse("(((((((((alternate(X0) AND NOT(bar(X0))) AND NOT(fri_sat(X0))) AND hungry(X0)) AND patrons(X0,Full)) AND price(X0,$)) AND NOT(raining(X0))) AND NOT(reservation(X0))) AND type(X0,Thai)) AND wait_estimate(X0,_30_60))");
            Sentence classification = parser.parse("will_wait(X0)");

            FOLKnowledgeBase kb = new FOLKnowledgeBase(domain,
                    new FOLOTTERLikeTheoremProver(false));

            kb.tell(hypothesis);
            kb.tell(desc);

            InferenceResult ir = kb.ask(classification);

            Assert.IsFalse(ir.isTrue());
            Assert.IsTrue(ir.isPossiblyFalse());
            Assert.IsFalse(ir.isUnknownDueToTimeout());
            Assert.IsFalse(ir.isPartialResultDueToTimeout());
            Assert.AreEqual(0, ir.getProofs().Count);
        }