:- module(sim_pipes, [precepts_to_sim/2]). precepts_to_sim(Precepts,VWorld):- create_empty_sim(VWorld), run_narrative(VWorld, Precepts, VWorld). narrative_to_sim(Narrative,VWorld):- create_empty_sim(VWorld), run_narrative(VWorld, Narrative, VWorld). run_narrative(VWorldIn, Narrative, VWorldOut):- forall(elementOf(E,Narrative), (resolve_narrative(E,VWorldIn,E2), add_narrative(E2,VWorldOut))). % currently these are the same precepts_to_narrative(Precepts, Narrative):- copy_term(Precepts, Narrative). narrative_to_precepts(Precepts, Narrative):- copy_term(Narrative, Precepts). create_empty_sim(VWorld):- copy_prolog_sim(empty_sim, VWorld). copy_prolog_sim(World1, World2):- object_to_props(sim, World1, World1Props), copy_term(World1Props,World2Props), create_object(sim, World2, World2Props). create_object(Type, Obj, ObjProps):- (\+ ground(Obj)->gen_obj_sym(Type, Obj); true), setprops(Obj,type(Type)), setprops(Obj,ObjProps). %LOGICMOO perceives events in the real world (well in PrologMUD!) which become event sequences (we will call narratives). ?- copy_prolog_sim(real_word, RealWorld), sim_get_precepts(RealWorld, Precepts), precepts_to_narrative(Precepts, Narrative). %Narratives may be replayed to create a copy of that of that original PrologMUD ?- narrative_to_sim($Narrative, VWorld). %This PrologMUD can be thought of as a Virtual World which LOGICMOO hallucinates. ?- copy_prolog_sim($VWorld, Hallucination). %This Virtual World can be re-experienced just the previous world was (as perceived events.) ?- sim_get_precepts($Hallucination, HallucinatedPrecepts). %Those perceived events can be re- “played” to create a copy in which LOGICMOO hallucinates yet another Virtual World of the Virtual World. ?- precepts_to_sim($HallucinatedPrecepts,HallucinatedVWorld). %This can be done indefinitely; those copies may become simpler or more complex (will be explained later (as pipelines)) %The event sequences (which are narratives) are equivalent to “internal dialog “ %“internal dialog” may be modified and then “played back” to created Imagined Worlds %An Imagined World can be re-experienced and thus create a new set of perceived Imagined events %Those perceived Imagined events can be re-“played” to create a copies of those Imagined Worlds %Those Imagined Worlds can be compared to each other the differences can constitute a hybrid PrologMUD %Internal dialog can be compared to other narratives without involving worlds at all. %The differences can be made into other narratives (thus internal dialogs) %This is used to generalize, specialize or condense Internal Dialogs. %It also may further isolate out the actions and recombine them to perceive them as new action sequences %Those perceived action sequences can be “played” into copies of PrologMUD %We may create pipelines between the above elements %Those elements again are: Events, Internal Dialog, Actions, %Pipelines may combine, split and recombine these into Events/Actions, and Internal Dialog  %Thus creating Worlds/PrologMUDs/Imagined Worlds. %Increasing and decreasing specificity within the narrative pipelines %Can produce both generalized and condensed versions of Internal dialog. %Douglas Miles claims this was integral to his solving The Egg Cracking problem % Parameters of the autoencoder input_size(4). % Number of input features hidden_size(2). % Number of hidden units output_size(4). % Number of output features learning_rate(0.1). % Learning rate for gradient descent % Initialize the weights of the encoder and the decoder initialize_weights :- input_size(InputSize), hidden_size(HiddenSize), output_size(OutputSize), random_matrix(InputSize, HiddenSize, EncoderWeights), random_matrix(HiddenSize, OutputSize, DecoderWeights), assert(encoder_weights(EncoderWeights)), assert(decoder_weights(DecoderWeights)). % Encode the input into a lower-dimensional representation encode(Input, Hidden) :- encoder_weights(EncoderWeights), dot_product(Input, EncoderWeights, Hidden), sigmoid(Hidden). % Decode the lower-dimensional representation into the output decode(Hidden, Output) :- decoder_weights(DecoderWeights), dot_product(Hidden, DecoderWeights, Output), sigmoid(Output). % Train the autoencoder using backpropagation train(Input, Output) :- encode(Input, Hidden), decode(Hidden, Reconstructed), loss(Input, Reconstructed, Loss), backpropagate(Hidden, Loss, HiddenGradients), backpropagate(Input, HiddenGradients, InputGradients), update_weights(HiddenGradients, InputGradients). % Compute the loss function loss(Input, Reconstructed, Loss) :- subtract(Input, Reconstructed, Error), dot_product(Error, Error, SquaredError), Loss is SquaredError / 2. % Backpropagate the error gradients through the network backpropagate(Input, Gradients, InputGradients) :- encoder_weights(EncoderWeights), transpose(EncoderWeights, TransposedEncoderWeights), dot_product(Gradients, TransposedEncoderWeights, InputGradients), derivative(Input, HiddenGradients, sigmoid), hadamard_product(InputGradients, HiddenGradients, InputGradients). backpropagate(Hidden, Gradients, HiddenGradients) :- decoder_weights(DecoderWeights), transpose(DecoderWeights, TransposedDecoderWeights), dot_product(Gradients, TransposedDecoderWeights, HiddenGradients), derivative(Hidden, OutputGradients, sigmoid), hadamard_product(HiddenGradients, OutputGradients, HiddenGradients). % Update the weights using gradient descent update_weights(HiddenGradients, InputGradients) :- learning_rate(LearningRate), encoder_weights(EncoderWeights), decoder_weights(DecoderWeights), outer_product(InputGradients, HiddenGradients, EncoderWeightGradients), outer_product(HiddenGradients, InputGradients, DecoderWeightGradients), scalar_multiply(EncoderWeightGradients, -LearningRate, ScaledEncoderWeightGradients), scalar_multiply(DecoderWeightGradients, -LearningRate, ScaledDecoderWeightGradients), add(EncoderWeights, ScaledEncoderWeightGradients, UpdatedEncoderWeights), add(DecoderWeights, ScaledDecoderWeights, ScaledDecoderWeightGradients, UpdatedDecoderWeights), retract(encoder_weights(_)), retract(decoder_weights(_)), assert(encoder_weights(UpdatedEncoderWeights)), assert(decoder_weights(UpdatedDecoderWeights)). % Helper predicates for matrix operations dot_product(Matrix1, Matrix2, Result) :- matrix_multiply(Matrix1, Matrix2, Result). hadamard_product(Matrix1, Matrix2, Result) :- matrix_elementwise_multiply(Matrix1, Matrix2, Result). outer_product(Vector1, Vector2, Result) :- matrix_multiply(Vector1, transpose(Vector2), Result). scalar_multiply(Matrix, Scalar, Result) :- matrix_scalar_multiply(Matrix, Scalar, Result). add(Matrix1, Matrix2, Result) :- matrix_add(Matrix1, Matrix2, Result). % Helper predicate for computing the derivative of the activation function derivative(Input, Output, sigmoid) :- sigmoid(Input, Output), hadamard_product(Output, subtract(1, Output), Output). % Helper predicate for computing the logistic sigmoid function sigmoid(X, Y) :- Y is 1 / (1 + exp(-X)). % Helper predicates for matrix manipulation random_matrix(Rows, Cols, Matrix) :- length(Matrix, Rows), maplist(random_list(Cols), Matrix). random_list(Size, List) :- length(List, Size), maplist(random_value, List). random_value(Value) :- random(Value). transpose(Matrix, Transposed) :- length(Matrix, Rows), length(Transposed, Cols), maplist(length_list(Rows), Transposed), transpose_helper(Matrix, Transposed). transpose_helper([], []). transpose_helper([Row|Rows], Transposed) :- maplist(list_head_tail, [Row|Rows], Heads, Tails), maplist(transpose_helper, Tails, TransposedHeads), append(Heads, TransposedHeads, Transposed). length_list(Len, List) :- length(List, Len). list_head_tail([Head|Tail], Head, Tail). matrix_multiply(Matrix1, Matrix2, Result) :- transpose(Matrix2, Transposed), maplist(dot_product_helper(Transposed), Matrix1, Result). dot_product_helper(Matrix, Vector, Result) :- dot_product(Matrix, Vector, Result). matrix_elementwise_multiply(Matrix1, Matrix2, Result) :- maplist(elementwise_multiply_helper, Matrix1, Matrix2, Result). elementwise_multiply_helper(Element1, Element2, Result) :- Result is Element1 * Element2. matrix_scalar_multiply(Matrix, Scalar, Result) :- maplist(scalar_multiply_helper(Scalar), Matrix, Result). scalar_multiply_helper(Scalar, Element, Result) :- Result is Scalar * Element. matrix_add(Matrix1, Matrix2, Result) :- maplist(add_helper, Matrix1, Matrix2, Result). add_helper(Element1, Element2, Result) :- Result is Element1 + Element2. /* This implementation uses some helper predicates for matrix manipulation, including matrix multiplication, element-wise multiplication, scalar multiplication, and matrix addition. These predicates are not built-in Prolog predicates, so they need to be defined explicitly. To use this autoencoder, you can call `initialize_weights` to initialize the weights of the encoder and the decoder, and then call `train` with your input data to train the autoencoder. You can also call `encode` to encode your input data into a lower-dimensional representation, and `decode` to decode the representation back into the original data. Note that this implementation is a simple proof-of-concept and is not optimized for performance. There are many ways to improve this */ % Define the n-gram size ngram_size(2). % Read in the corpus and tokenize it read_corpus(File, Tokens) :- read_file_to_string(File, String, []), split_string(String, "\s\t\n", "\s\t\n", Tokens). % Compute the n-grams of the corpus compute_ngrams(Tokens, Ngrams) :- ngram_size(N), length(Tokens, Len), MaxIndex is Len - N + 1, findall(Ngram, (between(1, MaxIndex, Index), nth1(Index, Tokens, First), length(Ngram, N), append([First], Rest, Ngram), nth1(Index2, Tokens, Rest), succ(Index, Index2)), Ngrams). % Train the language model on the corpus train_model(File) :- read_corpus(File, Tokens), compute_ngrams(Tokens, Ngrams), assert_ngrams(Ngrams). % Store the n-grams in the knowledge base assert_ngrams([]). assert_ngrams([Ngram|Ngrams]) :- increment_count(Ngram), assert_ngrams(Ngrams). % Increment the count of an n-gram in the knowledge base increment_count(Ngram) :- ngram_count(Ngram, Count), NewCount is Count + 1, retract(ngram_count(Ngram, Count)), assert(ngram_count(Ngram, NewCount)), !. increment_count(Ngram) :- assert(ngram_count(Ngram, 1)). % Predict the next word given a sequence of words predict_next(Sequence, Next) :- ngram_size(N), length(Sequence, Len), PrefixSize is N - 1, (Len >= PrefixSize -> append(Prefix, [Last], Sequence), findall(Count-Word, (ngram_count(Ngram, Count), append(Prefix, [Word], Ngram)), Pairs), keysort(Pairs, SortedPairs), reverse(SortedPairs, [MaxCount-Next|_]) ; Next = "" ). % Reset the knowledge base reset_model :- retractall(ngram_count(_, _)). % Export the model as a file export_model(File) :- tell(File), listing(ngram_count), told. % Here's an example usage of the language model: % Train the model on a corpus file ?- train_model('corpus.txt'). % Predict the next word given a sequence of words ?- predict_next(["the", "quick"], Next). % returns "brown" % Reset the knowledge base ?- reset_model(). % Export the model to a file ?- export_model('model.pl'). %implementation is a simple proof-of-concept and can be extended to handle larger n-grams, handle unseen words, and use smoothing techniques to handle zero counts. %In this example, we train the language model on a corpus file using the train_model/1 predicate. We then use the predict_next/2 predicate to predict the next word given a sequence of words ("the" and "quick"). The predicted next word is "brown". We can also reset the knowledge base using the reset_model/0 predicate and export the model to a file using the export_model/1 predicate. %Note that this implementation is just a basic example of a language model in Prolog, and there are many ways to improve and extend it. For example, we can use higher-order n-grams, handle out-of-vocabulary words, use more sophisticated smoothing techniques, and integrate the language model into a larger natural language processing pipeline. /* ngram_count(["the", "quick"], 5). ngram_count(["quick", "brown"], 3). ngram_count(["brown", "fox"], 2). ngram_count(["fox", "jumps"], 4). ngram_count(["jumps", "over"], 1). ngram_count(["over", "the"], 3). ngram_count(["the", "lazy"], 2). ngram_count(["lazy", "dog"], 1). */