112 lines
4.6 KiB
Plaintext
112 lines
4.6 KiB
Plaintext
Prolog Factor Language (PFL)
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Prolog Factor Language (PFL) is a extension of the Prolog language that
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allows a natural representation of this first-order probabilistic models
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(either directed or undirected). PFL is also capable of solving probabilistic
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queries on this models through the implementation of several inference
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techniques: variable elimination, belief propagation, lifted variable
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elimination and lifted belief propagation.
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Language
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-------------------------------------------------------------------------------
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A graphical model in PFL is represented using parfactors. A PFL parfactor
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has the following four components:
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Type ; Formulas ; Phi ; Constraint .
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- Type refers the type of the network over which the parfactor is defined.
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It can be bayes for directed networks, or markov for undirected ones.
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- Formulas is a sequence of Prolog terms that define sets of random variables
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under the constraint.
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- Phi is either a list of parameters or a call to a Prolog goal that will
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unify its last argument with a list of parameters.
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- Constraint is a list (possible empty) of Prolog goals that will impose
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bindings on the logical variables that appear in the formulas.
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The "examples" directory contains some popular graphical models described
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using PFL.
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Querying
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-------------------------------------------------------------------------------
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Now we show how to use PFL to solve probabilistic queries. We will
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use the burlgary alarm network as an example. First, we load the model:
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$ yap -l examples/burglary-alarm.yap
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Now let's suppose that we want to estimate the probability of a earthquake
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ocurred given that mary called. We can do it with the following query:
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?- earthquake(X), mary_calls(t).
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Suppose now that we want the joint distribution for john_calls and
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mary_calls. We can obtain this with the following query:
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?- john_calls(X), mary_calls(Y).
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Inference Options
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-------------------------------------------------------------------------------
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PFL supports both ground and lifted inference. The inference algorithm
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can be chosen using the set_solver/1 predicate. The following algorithms
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are supported:
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- lve: generalized counting first-order variable elimination (GC-FOVE)
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- hve: (ground) variable elimination
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- lbp: lifted first-order belief propagation
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- cbp: counting belief propagation
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- bp: (ground) belief propagation
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- lkc: lifted first-order knowledge compilation
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For example, if we want to use ground variable elimination to solve some
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query, we need to call first the following goal:
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?- set_solver(hve).
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It is possible to tweak several parameters of PFL through the
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set_horus_flag/2 predicate. The first argument is a key that
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identifies the parameter that we desire to tweak, while the second
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is some possible value for this key.
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The verbosity key controls the level of log information that will be
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printed by the corresponding solver. Its possible values are positive
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integers. The bigger the number, more log information will be printed.
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For example, to view some basic log information we need to call the
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following goal:
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?- set_horus_flag(verbosity, 1).
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The use_logarithms key controls whether the calculations performed
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during inference should be done in the log domain or not. Its values
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can be true or false. By default is false.
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There are also keys specific to the inference algorithm. For example,
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elim_heuristic key controls the elimination heuristic that will be
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used by ground variable elimination. The following heuristics are
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supported:
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- sequential
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- min_neighbors
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- min_weight
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- min_fill
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- weighted_min_fill
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An explanation of this heuristics can be found in Probabilistic Graphical
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Models by Daphne Koller.
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The schedule, accuracy and max_iter keys are specific for inference
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algorithms based on message passing, namely lbp, cbp and bp.
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The key schedule can be used to specify the order in which the messages
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are sent in belief propagation. The possible values are:
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- seq_fixed: at each iteration, all messages are sent in the same order
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- seq_random: at each iteration, the messages are sent with a random order
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- parallel: at each iteration, the messages are all calculated using the
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values of the previous iteration.
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- max_residual: the next message to be sent is the one with maximum residual,
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(Residual Belief Propagation:Informed Scheduling for Asynchronous Message
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Passing)
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The max_iter key sets the maximum number of iterations. One iteration
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consists in sending all possible messages. The accuracy key indicate
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when we should stop sending messages. If the largest difference between
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a message sent in the current iteration and one message sent in the previous
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iteration is less that accuracy value given, we terminate belief propagation.
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