clarion.system
Class TaskClarion

java.lang.Object
  extended byclarion.system.TaskClarion
Direct Known Subclasses:
TaskClarionAGL, TaskClarionPC, TaskClarionXOR

public class TaskClarion
extends java.lang.Object


Field Summary
static int ACS
          constants for CLARION subsystems.
static int ACS_CL
           
static int ACS_GAUGE
           
static int ACS_IN_DIM
           
static int ACS_IN_VAL
           
static int ACS_LEARN
           
static int ACS_OUT_DIM
           
static int ACS_OUT_VAL
           
static int ACS_PARAM
           
static int ACS_PERFORM
           
static int ACS_REASON
           
static int ACS_SAMPLE_NUM
           
static int AMN_PASS
           
static int ASSOC_APP
           
static int ASSOC_WGT_C
           
static int ASSOC_WGT_R
           
static int AT_TL
           
static int BL_RT
           
static int BUR
           
static int CHUNK_RETR
           
static int DATA_TYPE
           
static int DRIVE
           
static int DRV_REP
           
static int DT_TL
           
static int ELIG
           
static int EX_CHUNK
           
static int EX_RULE
           
static int FR_ACT
           
static int FR_CON
           
static int FR_UTL
           
static int FR_WGT_C
           
static int FR_WGT_R
           
protected  Global global
          the associated Global object.
static int GOAL
           
static int GOAL_DIM
           
static int GOAL_SETTING
           
static int IRL_IG
           
static int IRL_PARAM
           
static int IRL_POS
           
static int IRL_UTL
           
static int IRL_WGT_C
           
static int IRL_WGT_R
           
static int MCS
          constants for CLARION subsystems.
static int MCS_SAMPLE_NUM
           
static int NACS
          constants for CLARION subsystems.
static int NACS_GAUGE
           
static int NACS_IN_DIM
           
static int NACS_IN_VAL
           
static int NACS_LEARN
           
static int NACS_OUT_DIM
           
static int NACS_OUT_VAL
           
static int NACS_PARAM
           
static int NACS_PERFORM
           
static int NACS_REASON
           
static int NACS_SAMPLE_NUM
           
static int OVER_RT
           
static int PT_TL
           
static int REIN
           
static int REIN_FUNC
           
static int RER_IG
           
static int RER_POS
           
static int RER_UTL
           
static int RER_WGT_C
           
static int RER_WGT_R
           
static int RT
          constants for CLARION subsystems.
static int RT_SAMPLE_NUM
           
static int SIM
           
static int SUB_SYS_NUM
          constants for CLARION subsystems.
static int TDG
           
static int TL_RT
           
 
Constructor Summary
TaskClarion()
           
 
Method Summary
 void combineByBottomUpRectification()
          bottom-up rectification process described in the Chapter on ACS in tutorial.
 void combineByTopDownGuidance()
          top-down guidance process described in the Chapter on ACS in tutorial.
 double decideFrAction(int groupIdx, int netIdx, int setIdx, Feature[] sensoryInput, GoalChunk gsItem, Chunk[] wmItems, short[][][] suggestedAction)
          Decides action by a Fixed Rule.
 short[] getAamDataPattern(int netIdx, int chunkIdx, int ruleIdx)
          Returns the types of data pettern for training AAM.
 int getAmnPassTime()
          Returns AMN one-pass time.
 int getAssocAppTime(double assocRuleBla)
          Returns associative rule application time.
 double[] getAssocDimWeights(int dimNum)
          Returns the weights for computing associative rule support from one condition chunk.
 double[] getAssocWeights(int condNum)
          Returns the weights for computing associative rule support from all of the condition chunks.
 int getAT_TL(boolean flag, int assocAppTime, int chunkRetrTime)
          Returns the Top-Level Actuation Time.
 int getBLOverallRT(int PT_BL, int DT_BL, int AT_BL)
          Returns the overall Bottom Level response time computed by the formula use defines.
 int getChunkRetrTime(double chunkBla)
          Returns the NACS chunk retrieval time.
 int getDT_TL(int DT_BL, int actionTime, double ruleBla, double chunkBla)
          Returns the Top-Level Decision Time.
 short[][][] getEligibility(int netIdx)
          Returns the eligible conditions of the specified network.
static java.lang.String getExplanation(int subSysIdx, int sampleIdx)
           
 GKSChunk[] getExternalNacsChunks()
          Returns the externally given NACS chunks.
 AssocRule[] getExternalNacsRules()
          Returns the externally given associative rules.
 short[][][] getFrAction(int groupIdx, int netIdx, int ruleIdx)
          Returns FR actions.
 short[][] getFrCondition(int groupIdx, int netIdx, int ruleIdx)
          Returns a FR condition.
 boolean getFrPositivity(int learnOption, double reward, double qDiscount, double newMaxQVal, double qVal, double threshold)
          Returns true if current step is positive or not according to FR positivity criterion, false otherwise.
 double getFrUtility(int netIdx, int setIdx, double PM, double NM)
          Returns the utility of a specified set of FR rules.
 double[] getFrWeights(int netIdx)
          Returns the FR dimensional weights used to calculate rule support.
 short[] getGoalDimDVs()
          Returns the goal dimension-value info.
 short[][] getGoals()
          Returns the goals.
 double getIrlIG(int netIdx, double PM, double NM, double threshold)
          Returns the Information Gain according to the IRL Information Gain criterion.
 boolean getIrlPositivity(int learnOption, double reward, double qDiscount, double newMaxQVal, double qVal, double threshold)
          Returns true if current step is positive or not according to IRL positivity criterion, false otherwise.
 IrlRuleForm getIrlRule(int groupIdx, int netIdx, int setIdx, int ruleIdx)
          Returns an IRL rule form corresponding to the rule index.
 double getIrlUtility(int netIdx, int setIdx, double PM, double NM)
          Returns the utility of a specified set of IRL rules.
 double[] getIrlWeights(int netIdx)
          Returns the IRL dimensional weights used to calculate rule support.
 java.lang.Object[] getNacsResults()
          new routine.
 int getOverallRT(int RT_BL, int RT_TL)
          Returns the overall response time computed by the formula use defines.
 int getPT_TL(int PT_BL)
          Returns Top-Level Perceptual Time.
 double getReinforcement(int netIdx, Feature[] input, Chunk[] wmItems, GoalChunk goalItem, short[][][] action, Feature[] newInput, Chunk[] newWmItems, GoalChunk newGoalItem)
          Returns the reinforcement of the specified ACS network.
 double getRerIG(int netIdx, double PMa, double NMa, double PMb, double NMb)
          Returns the Information Gain according to the RER Information Gain criterion.
 boolean getRerPositivity(int learnOption, double reward, double qDiscount, double newMaxQVal, double qVal, double threshold)
          Returns true if current step is positive or not according to RER positivity criterion, false otherwise.
 double getRerUtility(int netIdx, int setIdx, double PM, double NM)
          Returns the utility of a specified set of RER rules.
 double[] getRerWeights(int netIdx)
          Returns the RER dimensional weights used to calculate rule support.
static java.lang.String getSample(int subSysIdx, int sampleIdx)
          Returns the specified sample code.
 double getSimilarity(GKSChunk chunk1, GKSChunk chunk2)
          Returns the similarity between two chunks.
 int getTLOverallRT(int PT_TL, int DT_TL, int AT_TL)
          Returns the overall Top Level response time computed by the formula use defines.
 void performEncodeExternalKnowledge(short[][] action)
          new routine.
 void performNacsAssimilation(short[][] action)
          new routine.
 void setGlobal(Global global)
           
 
Methods inherited from class java.lang.Object
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait
 

Field Detail

SUB_SYS_NUM

public static final int SUB_SYS_NUM
constants for CLARION subsystems.

See Also:
Constant Field Values

ACS

public static final int ACS
constants for CLARION subsystems.

See Also:
Constant Field Values

NACS

public static final int NACS
constants for CLARION subsystems.

See Also:
Constant Field Values

RT

public static final int RT
constants for CLARION subsystems.

See Also:
Constant Field Values

MCS

public static final int MCS
constants for CLARION subsystems.

See Also:
Constant Field Values

ACS_SAMPLE_NUM

public static final int ACS_SAMPLE_NUM
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NACS_SAMPLE_NUM

public static final int NACS_SAMPLE_NUM
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RT_SAMPLE_NUM

public static final int RT_SAMPLE_NUM
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MCS_SAMPLE_NUM

public static final int MCS_SAMPLE_NUM
See Also:
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GOAL

public static final int GOAL
See Also:
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GOAL_DIM

public static final int GOAL_DIM
See Also:
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BUR

public static final int BUR
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TDG

public static final int TDG
See Also:
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ELIG

public static final int ELIG
See Also:
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REIN

public static final int REIN
See Also:
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RER_WGT_C

public static final int RER_WGT_C
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RER_WGT_R

public static final int RER_WGT_R
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RER_UTL

public static final int RER_UTL
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RER_POS

public static final int RER_POS
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RER_IG

public static final int RER_IG
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IRL_WGT_C

public static final int IRL_WGT_C
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IRL_WGT_R

public static final int IRL_WGT_R
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IRL_UTL

public static final int IRL_UTL
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IRL_POS

public static final int IRL_POS
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IRL_IG

public static final int IRL_IG
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IRL_PARAM

public static final int IRL_PARAM
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FR_WGT_C

public static final int FR_WGT_C
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FR_WGT_R

public static final int FR_WGT_R
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FR_UTL

public static final int FR_UTL
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FR_CON

public static final int FR_CON
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FR_ACT

public static final int FR_ACT
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EX_CHUNK

public static final int EX_CHUNK
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EX_RULE

public static final int EX_RULE
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ASSOC_WGT_C

public static final int ASSOC_WGT_C
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ASSOC_WGT_R

public static final int ASSOC_WGT_R
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SIM

public static final int SIM
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DATA_TYPE

public static final int DATA_TYPE
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OVER_RT

public static final int OVER_RT
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BL_RT

public static final int BL_RT
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TL_RT

public static final int TL_RT
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PT_TL

public static final int PT_TL
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DT_TL

public static final int DT_TL
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AT_TL

public static final int AT_TL
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ASSOC_APP

public static final int ASSOC_APP
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CHUNK_RETR

public static final int CHUNK_RETR
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AMN_PASS

public static final int AMN_PASS
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DRIVE

public static final int DRIVE
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DRV_REP

public static final int DRV_REP
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REIN_FUNC

public static final int REIN_FUNC
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GOAL_SETTING

public static final int GOAL_SETTING
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ACS_IN_DIM

public static final int ACS_IN_DIM
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NACS_IN_DIM

public static final int NACS_IN_DIM
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ACS_OUT_DIM

public static final int ACS_OUT_DIM
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NACS_OUT_DIM

public static final int NACS_OUT_DIM
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ACS_IN_VAL

public static final int ACS_IN_VAL
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NACS_IN_VAL

public static final int NACS_IN_VAL
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ACS_OUT_VAL

public static final int ACS_OUT_VAL
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NACS_OUT_VAL

public static final int NACS_OUT_VAL
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ACS_LEARN

public static final int ACS_LEARN
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NACS_LEARN

public static final int NACS_LEARN
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ACS_REASON

public static final int ACS_REASON
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NACS_REASON

public static final int NACS_REASON
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ACS_CL

public static final int ACS_CL
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ACS_PARAM

public static final int ACS_PARAM
See Also:
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NACS_PARAM

public static final int NACS_PARAM
See Also:
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ACS_PERFORM

public static final int ACS_PERFORM
See Also:
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NACS_PERFORM

public static final int NACS_PERFORM
See Also:
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ACS_GAUGE

public static final int ACS_GAUGE
See Also:
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NACS_GAUGE

public static final int NACS_GAUGE
See Also:
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global

protected Global global
the associated Global object.

Constructor Detail

TaskClarion

public TaskClarion()
Method Detail

getSample

public static java.lang.String getSample(int subSysIdx,
                                         int sampleIdx)
Returns the specified sample code.

Parameters:
subSysIdx - the index on CLARION subsystem.
sampleIdx - the index on the sample within the specified subsystem.
Returns:
the specified sample code in the format of String.

getExplanation

public static java.lang.String getExplanation(int subSysIdx,
                                              int sampleIdx)

setGlobal

public void setGlobal(Global global)

getGoals

public short[][] getGoals()
Returns the goals. The returned value is in the format of 2-dimenstional array. The first dimension indexes a specific goal and the second dimension indexes a specific goal dimension. We assume that a goal is composed by an array of values each corresponding to a specific goal dimension.

Returns:
the goal set.

getGoalDimDVs

public short[] getGoalDimDVs()
Returns the goal dimension-value info. The returned value is in the foramt of one dimensional array each slot storing the corresponding dimension value #.

Returns:
the goal dimension-value info

combineByBottomUpRectification

public void combineByBottomUpRectification()
bottom-up rectification process described in the Chapter on ACS in tutorial.


combineByTopDownGuidance

public void combineByTopDownGuidance()
top-down guidance process described in the Chapter on ACS in tutorial.


getEligibility

public short[][][] getEligibility(int netIdx)
Returns the eligible conditions of the specified network. The return value is in the format of 3-dimensional array. The first dimension indexes an eligible condition, the second dimension indexes an involved input dimension and the third dimnesion indexes a value in the involved dimension. Note: the first element of the second dimension stores all of the involved input dimensions.

Parameters:
netIdx - the ACS network NO.
Returns:
the eligible condition of the specified network.

getReinforcement

public double getReinforcement(int netIdx,
                               Feature[] input,
                               Chunk[] wmItems,
                               GoalChunk goalItem,
                               short[][][] action,
                               Feature[] newInput,
                               Chunk[] newWmItems,
                               GoalChunk newGoalItem)
Returns the reinforcement of the specified ACS network.

Parameters:
netIdx - the network NO.
input - current input,
action - current action.
newInput - new input resulting from firing the action.
Returns:
the reinforcement of the specified ACS network.

getRerWeights

public double[] getRerWeights(int netIdx)
Returns the RER dimensional weights used to calculate rule support. The returned value is in one dimensional array storing the weights of the input dimensions. Note: the sum of the weights should be 1.0.

Parameters:
netIdx - the ACS network NO.
Returns:
the dimensional RER weights.

getRerUtility

public double getRerUtility(int netIdx,
                            int setIdx,
                            double PM,
                            double NM)
Returns the utility of a specified set of RER rules.

Parameters:
netIdx - the network NO.
setIdx - the rule subset NO.
PM - Positive Match of the rule, equals the number of times that an input state matches rule condition, rule action is performed and the result is positive.
NM - Negative Match of the rule, equals the number of times that an input state matches rule condition, rule action is performed and the result is negative.
Returns:
the utility of a specified set of RER rules.

getRerPositivity

public boolean getRerPositivity(int learnOption,
                                double reward,
                                double qDiscount,
                                double newMaxQVal,
                                double qVal,
                                double threshold)
Returns true if current step is positive or not according to RER positivity criterion, false otherwise.

Parameters:
learnOption - learning method.
reward - currently received reward.
qDiscount - the discount factor for Q-Learning that favors reinfocement received sooner relative to that received later. used in q-learning method.
newMaxQVal - maximum Q-value of the new state resuling from current action in current state, Q(next state, next possible action). used in q-learning method.
qVal - current q-value, Q(current state, current action).
threshold - the threshold for rule positivity.
Returns:
if current step is positive or not according to RER positivity.

getRerIG

public double getRerIG(int netIdx,
                       double PMa,
                       double NMa,
                       double PMb,
                       double NMb)
Returns the Information Gain according to the RER Information Gain criterion.

Parameters:
netIdx - the network NO. decdies the values of c1 and c2.
PMa - Positive Match of the rule a, equals the number of times that an input state matches rule condition, rule action is performed and the result is positive.
NMa - Negative Match of the rule a, equals the number of times that an input state matches rule condition, rule action is performed and the result is negative.
PMb - Positive Match of the rule b.
NMb - Negative Match of the rule b.
Returns:
the Information Gain.

getIrlWeights

public double[] getIrlWeights(int netIdx)
Returns the IRL dimensional weights used to calculate rule support. The returned value is in one dimensional array storing the weights of the input dimensions. Note: the sum of the weights should be 1.0.

Parameters:
netIdx - the ACS network NO.
Returns:
the dimensional IRL weights.

getIrlUtility

public double getIrlUtility(int netIdx,
                            int setIdx,
                            double PM,
                            double NM)
Returns the utility of a specified set of IRL rules.

Parameters:
netIdx - the network NO.
setIdx - the rule subset NO.
PM - Positive Match of the rule, equals the number of times that an input state matches rule condition, rule action is performed and the result is positive.
NM - Negative Match of the rule, equals the number of times that an input state matches rule condition, rule action is performed and the result is negative.
Returns:
the utility of a specified set of IRL rules.

getIrlPositivity

public boolean getIrlPositivity(int learnOption,
                                double reward,
                                double qDiscount,
                                double newMaxQVal,
                                double qVal,
                                double threshold)
Returns true if current step is positive or not according to IRL positivity criterion, false otherwise.

Parameters:
learnOption - learning method.
reward - currently received reward.
qDiscount - the discount factor for Q-Learning that favors reinfocement received sooner relative to that received later. used in q-learning method.
newMaxQVal - maximum Q-value of the new state resuling from current action in current state, Q(next state, next possible action). used in q-learning method.
qVal - current q-value, Q(current state, current action).
threshold - the threshold for rule positivity.
Returns:
if current step is positive or not according to IRL positivity.

getIrlIG

public double getIrlIG(int netIdx,
                       double PM,
                       double NM,
                       double threshold)
Returns the Information Gain according to the IRL Information Gain criterion.

Parameters:
netIdx - the network NO. decdies the values of c5 and c6.
PM - Positive Match of the rule, equals the number of times that an input state matches rule condition, rule action is performed and the result is positive.
NM - Negative Match of the rule, equals the number of times that an input state matches rule condition, rule action is performed and the result is negative.
threshold - compared threshold value.
Returns:
the Information Gain.

getIrlRule

public IrlRuleForm getIrlRule(int groupIdx,
                              int netIdx,
                              int setIdx,
                              int ruleIdx)
Returns an IRL rule form corresponding to the rule index.

Parameters:
groupIdx - agent group index.
netIdx - ACS network index.
setIdx - rule subset index.
ruleIdx - rule index.
Returns:
an IRL rule form corresponding to the rule index.

getFrWeights

public double[] getFrWeights(int netIdx)
Returns the FR dimensional weights used to calculate rule support. The returned value is in one dimensional array storing the weights of the input dimensions. Note: the sum of the weights should be 1.0.

Parameters:
netIdx - the ACS network NO.
Returns:
the dimensional FR weights.

getFrUtility

public double getFrUtility(int netIdx,
                           int setIdx,
                           double PM,
                           double NM)
Returns the utility of a specified set of FR rules.

Parameters:
netIdx - the network NO.
setIdx - the rule subset NO.
PM - Positive Match of the rule, equals the number of times that an input state matches rule condition, rule action is performed and the result is positive.
NM - Negative Match of the rule, equals the number of times that an input state matches rule condition, rule action is performed and the result is negative.
Returns:
the utility of a specified set of FR rules.

getFrPositivity

public boolean getFrPositivity(int learnOption,
                               double reward,
                               double qDiscount,
                               double newMaxQVal,
                               double qVal,
                               double threshold)
Returns true if current step is positive or not according to FR positivity criterion, false otherwise.

Parameters:
learnOption - learning method.
reward - currently received reward.
qDiscount - the discount factor for Q-Learning that favors reinfocement received sooner relative to that received later. used in q-learning method.
newMaxQVal - maximum Q-value of the new state resuling from current action in current state, Q(next state, next possible action). used in q-learning method.
qVal - current q-value, Q(current state, current action).
threshold - the threshold for rule positivity.
Returns:
if current step is positive or not according to FR positivity.

getFrCondition

public short[][] getFrCondition(int groupIdx,
                                int netIdx,
                                int ruleIdx)
Returns a FR condition. The returned value is in the format of 2-dimenisonal array. The first dimension indexes an dimenison and the second indexes one deimensional value. The value in each slot of the returned array: 1, if the dimensional value is in the condition, 0, otherwise.

Parameters:
groupIdx - agent group index.
netIdx - ACS network index.
ruleIdx - rule index.
Returns:
a FR condition.

getFrAction

public short[][][] getFrAction(int groupIdx,
                               int netIdx,
                               int ruleIdx)
Returns FR actions. The returned value is in the format of 3-dimenisonal array. The first dimension indicates action type : index = 0 : normal action, index = 1: NACS control action. The second dimension indexes an action dimension. The third stores the active values in that dimension.

Parameters:
groupIdx - learniing group the agent is in.
netIdx - the net index.
ruleIdx - the rule index.
Returns:
the action of the specific fixed rule.

decideFrAction

public double decideFrAction(int groupIdx,
                             int netIdx,
                             int setIdx,
                             Feature[] sensoryInput,
                             GoalChunk gsItem,
                             Chunk[] wmItems,
                             short[][][] suggestedAction)
Decides action by a Fixed Rule. It is used for the action (of a Fixed Rule) which is variable and depends on current input (EX + GS + WM). Fills in the suggestedAction under current input and returns the rule support for current input.

Parameters:
groupIdx - learniing group the agent is in.
netIdx - the net index.
setIdx - the rule set index.
sensoryInput - current sensory input.
gsItem - the top goal item.
wmItems - current WM content.
Returns:
the reinforcement.

getExternalNacsChunks

public GKSChunk[] getExternalNacsChunks()
Returns the externally given NACS chunks.

Returns:
the externally given NACS chunks.

getExternalNacsRules

public AssocRule[] getExternalNacsRules()
Returns the externally given associative rules.

Returns:
the externally given associative rules.

getAssocWeights

public double[] getAssocWeights(int condNum)
Returns the weights for computing associative rule support from all of the condition chunks. the sum of the weights should be 1.0.

Returns:
the weights in the format of an array each indexes a condition.

getAssocDimWeights

public double[] getAssocDimWeights(int dimNum)
Returns the weights for computing associative rule support from one condition chunk. the sum of the weights should be 1.0.

Returns:
the weights in the format of an array each indexes a dimension.

getSimilarity

public double getSimilarity(GKSChunk chunk1,
                            GKSChunk chunk2)
Returns the similarity between two chunks. The chunk similarity measure is similar to: # of equal dimensions between chunk1 and chunk2 / # of dimensions in chunk2.

Parameters:
chunk1 - the source chunk.
chunk2 - the destination chunk.
Returns:
the similarity.

getAamDataPattern

public short[] getAamDataPattern(int netIdx,
                                 int chunkIdx,
                                 int ruleIdx)
Returns the types of data pettern for training AAM.

Parameters:
netIdx - the AAM net index.
chunkIdx - the chunk the rule is associated with.
ruleIdx - the rule index on the rule group associated with the chunk.
Returns:
the types of data pettern for training AAM.

performEncodeExternalKnowledge

public void performEncodeExternalKnowledge(short[][] action)
new routine. Performs encoding external knowledge in NACS.


performNacsAssimilation

public void performNacsAssimilation(short[][] action)
new routine. Performs the assimilation in NACS.


getNacsResults

public java.lang.Object[] getNacsResults()
new routine. Gets retreival results from NACS.


getOverallRT

public int getOverallRT(int RT_BL,
                        int RT_TL)
Returns the overall response time computed by the formula use defines.

Parameters:
RT_BL - response time from Bottom Level.
RT_TL - response time from Top Level.
Returns:
overall response time.

getBLOverallRT

public int getBLOverallRT(int PT_BL,
                          int DT_BL,
                          int AT_BL)
Returns the overall Bottom Level response time computed by the formula use defines.

Parameters:
PT_BL - Bottom-Level Perceptual Time.
DT_BL - Bottom-Level Decision Time.
AT_BL - Bottom-Level Actuation Time.
Returns:
the overall Bottom Level response time.

getTLOverallRT

public int getTLOverallRT(int PT_TL,
                          int DT_TL,
                          int AT_TL)
Returns the overall Top Level response time computed by the formula use defines.

Returns:
the overall Top Level response time.

getPT_TL

public int getPT_TL(int PT_BL)
Returns Top-Level Perceptual Time.

Parameters:
PT_BL - Bottom-Level Perceptual Time.
Returns:
Top-Level Perceptual Time.

getDT_TL

public int getDT_TL(int DT_BL,
                    int actionTime,
                    double ruleBla,
                    double chunkBla)
Returns the Top-Level Decision Time.

Parameters:
DT_BL - Bottom-Level Decision Time.
actionTime - time determined by the operation dictated by a particular rule.
ruleBla - rule base-level activation.
chunkBla - action chunk base-level activation.
Returns:
the Top-Level Decision Time.

getAT_TL

public int getAT_TL(boolean flag,
                    int assocAppTime,
                    int chunkRetrTime)
Returns the Top-Level Actuation Time.

Parameters:
flag - decides if NACS is involved or not.
assocAppTime - time of one iteration of associative applicaiton.
chunkRetrTime - time of one iteration of chunk retrieval.

getAssocAppTime

public int getAssocAppTime(double assocRuleBla)
Returns associative rule application time.

Parameters:
assocRuleBla - associative rule base-level activation.
Returns:
associative rule application time.

getChunkRetrTime

public int getChunkRetrTime(double chunkBla)
Returns the NACS chunk retrieval time.

Parameters:
chunkBla - - nacs chunk base-level activation.
Returns:
the NACS chunk retrieval time.

getAmnPassTime

public int getAmnPassTime()
Returns AMN one-pass time.

Returns:
AMN one-pass time.