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266 lines
13 KiB
266 lines
13 KiB
/**
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* Engine.java
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* Copyright 2009 by Michael Peter Christen, Frankfurt a. M., Germany
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* First published 03.12.2009 at http://yacy.net
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*
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* $LastChangedDate$
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* $LastChangedRevision$
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* $LastChangedBy$
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*
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* This library is free software; you can redistribute it and/or
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* modify it under the terms of the GNU Lesser General Public
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* License as published by the Free Software Foundation; either
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* version 2.1 of the License, or (at your option) any later version.
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*
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* This library is distributed in the hope that it will be useful,
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* but WITHOUT ANY WARRANTY; without even the implied warranty of
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* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
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* Lesser General Public License for more details.
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*
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* You should have received a copy of the GNU Lesser General Public License
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* along with this program in the file lgpl21.txt
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* If not, see <http://www.gnu.org/licenses/>.
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*/
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package net.yacy.ai.greedy;
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import java.util.List;
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import java.util.PriorityQueue;
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import java.util.concurrent.ConcurrentHashMap;
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import java.util.concurrent.PriorityBlockingQueue;
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public class Engine<
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SpecificRole extends Role,
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SpecificFinding extends Finding<SpecificRole>,
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SpecificModel extends Model<SpecificRole, SpecificFinding>
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> {
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protected final PriorityBlockingQueue<Agent<SpecificRole, SpecificFinding, SpecificModel>> agentQueue;
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protected final PriorityBlockingQueue<Challenge<SpecificRole, SpecificFinding, SpecificModel>> challengeQueue;
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protected final Agent<SpecificRole, SpecificFinding, SpecificModel> poisonAgent;
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protected final Challenge<SpecificRole, SpecificFinding, SpecificModel> poisonChallenge;
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protected final ConcurrentHashMap<SpecificModel, List<SpecificFinding>> settings;
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protected final ConcurrentHashMap<Asset<SpecificRole, SpecificFinding, SpecificModel>, SpecificModel> assets;
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private final int cores;
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public Engine(int cores) {
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this.cores = cores;
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this.poisonAgent = new Agent<SpecificRole, SpecificFinding, SpecificModel>();
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this.poisonChallenge = new Challenge<SpecificRole, SpecificFinding, SpecificModel>();
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this.agentQueue = new PriorityBlockingQueue<Agent<SpecificRole, SpecificFinding, SpecificModel>>();
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this.challengeQueue = new PriorityBlockingQueue<Challenge<SpecificRole, SpecificFinding, SpecificModel>>();
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this.settings = new ConcurrentHashMap<SpecificModel, List<SpecificFinding>>();
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this.assets = new ConcurrentHashMap<Asset<SpecificRole, SpecificFinding, SpecificModel>, SpecificModel>();
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}
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public void start() {
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int c = (this.cores == 0) ? Runtime.getRuntime().availableProcessors() : this.cores;
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for (int i = 0; i < c; i++) {
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new SettingRunner().start();
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new AssetRunner().start();
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}
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}
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public void stop() {
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int c = (this.cores == 0) ? Runtime.getRuntime().availableProcessors() : this.cores;
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for (int i = 0; i < c; i++) {
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this.agentQueue.put(this.poisonAgent);
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this.challengeQueue.put(this.poisonChallenge);
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}
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}
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public void inject(Agent<SpecificRole, SpecificFinding, SpecificModel> agent) {
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agent.getContext().reset();
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this.agentQueue.put(agent);
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}
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public class SettingRunner extends Thread {
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@Override
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public void run() {
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Agent<SpecificRole, SpecificFinding, SpecificModel> agent;
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Challenge<SpecificRole, SpecificFinding, SpecificModel> challenge;
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Context<SpecificRole, SpecificFinding, SpecificModel> context;
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SpecificModel model;
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List<SpecificFinding> findings;
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PriorityQueue<Challenge<SpecificRole, SpecificFinding, SpecificModel>> preChallenge = new PriorityQueue<Challenge<SpecificRole, SpecificFinding, SpecificModel>>();
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try {
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while ((agent = agentQueue.take()) != poisonAgent) {
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// check termination of that goal
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context = agent.getContext();
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if (context.isCompleted()) continue;
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// produce findings in a setting environment and try to get it from a cache
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model = agent.getModel();
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findings = settings.get(model);
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if (findings == null) {
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findings = model.explore();
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settings.put(model, findings);
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}
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// branch
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for (SpecificFinding finding: findings) {
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challenge = new Challenge<SpecificRole, SpecificFinding, SpecificModel>(agent, finding);
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//System.out.println("finding: " + finding.toString() + ", priority: " + finding.getPriority());
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preChallenge.add(challenge);
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}
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challengefeeder: while (!preChallenge.isEmpty()) {
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if (context.isCompleted()) break challengefeeder;
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challengeQueue.put(preChallenge.poll());
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agent.incInstances();
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}
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//while (!challengeQueue.isEmpty()) System.out.println("finding: " + challengeQueue.take().getChallenge().getFinding().toString());
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}
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} catch (InterruptedException e) {
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e.printStackTrace();
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}
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}
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}
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public class AssetRunner extends Thread {
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@SuppressWarnings("unchecked")
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@Override
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public void run() {
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Challenge<SpecificRole, SpecificFinding, SpecificModel> challenge;
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Agent<SpecificRole, SpecificFinding, SpecificModel> agent, nextAgent;
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Goal<SpecificRole, SpecificFinding, SpecificModel> goal;
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Context<SpecificRole, SpecificFinding, SpecificModel> context;
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Asset<SpecificRole, SpecificFinding, SpecificModel> asset;
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SpecificRole role;
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SpecificModel nextModel = null;
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try {
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while ((challenge = challengeQueue.take()) != poisonChallenge) {
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assert challenge != null;
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agent = challenge.getAgent();
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agent.decInstances();
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context = agent.getContext();
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goal = context.getGoal();
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role = challenge.getFinding().getRole();
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// check termination by catching the global termination signal
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// and check expiration before applying finding
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// this shall not place the model to the results because
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// it has not the last finding assigned to the current user
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if (context.isCompleted()) continue;
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String debug = agent.getModel().toString() + "\nwill apply " + challenge.getFinding().toString();
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System.out.println(debug);
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if (debug.equals("[],[3, 2],[1]\nwill apply 2 -> 1")) {
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System.out.println("one more please");
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}
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// apply finding: compute next model
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// avoid double computation of findings using cached assets
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if (context.useAssetCache()) {
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asset = new Asset<SpecificRole, SpecificFinding, SpecificModel>(agent.getModel(), challenge.getFinding());
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nextModel = assets.get(asset);
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if (nextModel == null) {
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// generate model clone and apply finding
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try {
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nextModel = (SpecificModel) agent.getModel().clone();
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} catch (CloneNotSupportedException e) {
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e.printStackTrace();
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}
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nextModel.applyFinding(challenge.getFinding());
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nextModel.nextRole();
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if (context.feedAssetCache()) assets.put(asset, nextModel);
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}
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} else {
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// generate model clone and apply finding
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try {
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nextModel = (SpecificModel) agent.getModel().clone();
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} catch (CloneNotSupportedException e) {
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e.printStackTrace();
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}
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nextModel.applyFinding(challenge.getFinding());
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nextModel.nextRole();
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if (context.feedAssetCache()) {
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asset = new Asset<SpecificRole, SpecificFinding, SpecificModel>(agent.getModel(), challenge.getFinding());
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assets.put(asset, nextModel);
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}
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}
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// prune double-occurring models:
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// while it appears not to be a very good idea to produce models from the
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// asset cache above and then prune with double appearing models here, this is
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// still useful if the assets come from an earlier injection of an other agent.
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// Because the asset cache is a global cache owned by the engine it can produce
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// ready-computed models that not already exist in the agent cache
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if (agent.getContext().isKnownModel(nextModel)) {
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agent.checkInstanceCount();
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continue;
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}
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// place new model into agent and record finding
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nextAgent = new Agent<SpecificRole, SpecificFinding, SpecificModel>(agent, nextModel, challenge.getFinding());
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nextAgent.checkInstanceCount();
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// check if we arrived at a termination point
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SpecificRole terminationRole = nextModel.isTermination();
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if (terminationRole != null) {
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// the current role has a termination situation. In case that it is the start user, add a result
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nextAgent.addResult();
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if (goal.isFulfilled(nextAgent.getModel())) nextAgent.getContext().announceCompletion();
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// one of the roles has terminated.
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// prune this branch for other branches from the parent
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//System.out.println("terminationRole = " + terminationRole);
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if (agent.getFinding() == null) {
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// this is the root of the search tree: a fail in the search
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//agent.getContext().getGoal().announceFullfillment();
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} else {
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assert agent.getFinding() != null;
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assert agent.getFinding().getRole() != null;
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agent.setFindingFail();
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}
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//System.out.println("found winner model for " + terminationRole.toString() + ", latest finding: " + challenge.getFinding().toString() + "\n" + nextModel.toString());
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agent.checkInstanceCount();
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continue;
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}
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// check time-out for snapshot
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if (nextAgent.getContext().isSnapshotTimeout()) {
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nextAgent.addResult();
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}
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// check pruning
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if (goal.pruning(nextModel)) {
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agent.checkInstanceCount();
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continue;
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}
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// do not follow situations where it is known that somebody has made a fatal move in the past
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if (agent.isPrunedByTerminationInHistory() != null) {
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agent.checkInstanceCount();
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continue;
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}
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// check best move criteria
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int ranking = agent.getRanking(role);
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if (context.setBestMove(role, ranking)) {
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nextAgent.addResult();
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}
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// check snapshot
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if (goal.isSnapshot(nextModel)) {
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nextAgent.addResult();
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// no pruning here
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}
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if (context.hasNoResults()) nextAgent.addResult();
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if (context.isCompleted()) {
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continue;
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}
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// stack agent for next loop
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// using the priority of the next role
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agentQueue.put(nextAgent);
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}
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} catch (InterruptedException e) {
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e.printStackTrace();
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}
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}
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}
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}
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