Source code for graphbrain.cognition.agent

import logging
import progressbar

[docs]class Agent(object): """Base class for Graphbrain cognitive agents. These agents perform some change to an hypergraph, either by processing some external source of information of by inferring new knowledge from what is already contained in the hypregraph. """ def __init__(self, name, progress_bar=True, logging_level=logging.INFO): = name self.progress_bar = progress_bar self.logger = logging.getLogger( self.logger.setLevel(logging_level) self.search_pattern = '*' self.recursive = True self.system = None self.running = False
[docs] def languages(self): """Returns set of languages supported by the agent, or an empty set if the agent is language-agnostic. """ return set()
[docs] def process_edge(self, edge, depth): """Feeds the agent an edge to process.""" # do nothing by default return None
[docs] def input_edge(self, edge, depth=0): """Feeds the agent an edge to process.""" ops = self.process_edge(edge, depth) if ops: for op in ops: yield op # recursive step if self.recursive: if not edge.is_atom(): for subedge in edge: for op in self.input_edge(subedge, depth + 1): yield op
[docs] def on_start(self): """Called before a cycle of activity is started."""
[docs] def on_end(self): """Called at the end of a cycle of activity.""" return []
[docs] def input(self): """Input to the agent all the edges corresponding to its current search pattern. A typical use is in knowledge inference agents, which infer new knowledge from edges already present in the hypergraph. """ edge_count = self.system.hg.count(self.search_pattern) i = 0 if self.progress_bar: pbar = progressbar.ProgressBar(max_value=edge_count).start() else: pbar = None for edge in ops = self.input_edge(edge) if ops: for op in ops: yield op if i < edge_count: i += 1 if self.progress_bar: pbar.update(i) if self.progress_bar: pbar.finish()
[docs] def report(self): """Produce a report of the agent's activities.""" return ''
[docs] def run(self): """High-level method to run an agent.""" for op in self.input(): yield op