Processing device with intuitive learning capability

A method of providing learning capability to a computer game having an objective of matching a skill level of said computer game with a skill level of a game player, comprising:

• receiving an action performed by said game player;
• selecting one of a plurality of game actions based on an action probability distribution comprising a plurality of probability values corresponding to said plurality of game actions;
• determining an outcome value based on said player action and said selected game action;
• updating said action probability distribution based on said outcome value;
• and
• modifying one or more subsequent game action selections, outcome value determinations, and action probability distribution updates based on said objective.

A method of providing learning capability to a processing device, comprising:

• generating an action probability distribution comprising a plurality of probability values organized among a plurality of action subsets, said plurality of probability values corresponding to a plurality of processor actions;
• selecting one of said plurality of action subsets;
• and
• selecting one of said plurality of processor actions from said selected action subset.

A method of providing learning capability to a computer game, comprising:

• generating an action probability distribution comprising a plurality of probability values organized among a plurality of action subsets, said plurality of probability values corresponding to a plurality of game actions;
• selecting one of said plurality of action subsets;
• and
• selecting one of said plurality of game actions from said selected action subset.

A method of providing learning capability to a processing device, comprising:

• generating an action probability distribution using one or more learning algorithms, said action probability distribution comprising a plurality of probability values corresponding to a plurality of processor actions;
• modifying said one or more learning algorithms;
• and
• updating said action probability distribution using said modified one or more learning algorithms.

A method of providing learning capability to a computer game, comprising:

• generating an action probability distribution using one or more learning algorithms, said action probability distribution comprising a plurality of probability values corresponding to a plurality of game actions;
• modifying said one or more learning algorithms;
• and
• updating said action probability distribution using said modified one or more learning algorithms.

A method of matching a skill level of game player with a skill level of a computer game, comprising:

• receiving an action performed by said game player;
• selecting one of a plurality of game actions based on an action probability distribution comprising a plurality of probability values corresponding to said plurality of game actions;
• determining if said selected game action is successful;
• determining a current skill level of said game player relative to a current skill level of said computer game;
• and
• updating said action probability distribution using a reward algorithm if said selected game action is successful and said relative skill level is relatively high, or if said selected game action is unsuccessful and said relative skill level is relatively low.

A method of matching a skill level of game player with a skill level of a computer game, comprising:

• receiving an action performed by said game player;
• selecting one of a plurality of game actions based on an action probability distribution comprising a plurality of probability values corresponding to said plurality of game actions;
• determining if said selected game action is successful;
• determining a current skill level of said game player relative to a current skill level of said computer game;
• and
• updating said action probability distribution using a penalty algorithm if said selected game action is unsuccessful and said relative skill level is relatively high, or if said selected game action is successful and said relative skill level is relatively low.

A method of matching a skill level of game player with a skill level of a computer game, comprising:

• receiving an action performed by said game player;
• selecting one of a plurality of game actions based on an action probability distribution comprising a plurality of probability values corresponding to said plurality of game actions;
• determining if said selected game action is successful;
• determining a current skill level of said game player relative to a current skill level of said computer game;
• updating said action probability distribution using a reward algorithm if said selected game action is successful and said relative skill level is relatively high, or if said selected game action is unsuccessful and said relative skill level is relatively low;
• and
• updating said action probability distribution using a penalty algorithm if said selected game action is unsuccessful and said relative skill level is relatively high, or if said selected game action is successful and said relative skill level is relatively low.

A method of matching a skill level of game player with a skill level of a computer game, comprising:

• receiving an action performed by said game player;
• selecting one of a plurality of game actions based on an action probability distribution comprising a plurality of probability values corresponding to said plurality of game actions;
• determining if said selected game action is successful;
• determining a current skill level of said game player relative to a current skill level of said computer game;
• generating a successful outcome value if said selected game action is successful and said relative skill level is relatively high, or if said selected game action is unsuccessful and said relative skill level is relatively low;
• and
• updating said action probability distribution based on said successful outcome value.

A method of matching a skill level of game player with a skill level of a computer game, comprising:

• receiving an action performed by said game player;
• selecting one of a plurality of game actions based on an action probability distribution comprising a plurality of probability values corresponding to said plurality of game actions;
• determining if said selected game action is successful;
• determining a current skill level of said game player relative to a current skill level of said computer game;
• generating an unsuccessful outcome value if said selected game action is unsuccessful and said relative skill level is relatively high, or if said selected game action is successful and said relative skill level is relatively low;
• and
• updating said action probability distribution based on said unsuccessful outcome value.

A method of matching a skill level of game player with a skill level of a computer game, comprising:

• receiving an action performed by said game player;
• selecting one of a plurality of game actions based on an action probability distribution comprising a plurality of probability values corresponding to said plurality of game actions;
• determining if said selected game action is successful;
• determining a current skill level of said game player relative to a current skill level of said computer game;
• generating a successful outcome value if said selected game action is successful and said relative skill level is relatively high, or if said selected game action is successful and said relative skill level is relatively low;
• generating an unsuccessful outcome value if said selected game action is unsuccessful and said relative skill level is relatively high, or if said selected game action is successful and said relative skill level is relatively low;
• and
• updating said action probability distribution based on said successful outcome value and said unsuccessful outcome value.

A method of providing learning capability to a processing device, comprising:

• receiving an action performed by a user;
• selecting one of a plurality of processor actions based on an action probability distribution comprising a plurality of probability values corresponding to said plurality of processor actions;
• determining an outcome value based on one or both of said user action and said selected processor action;
• updating said action probability distribution based on said outcome value;
• and
• repeating said foregoing steps, wherein said action probability distribution is prevented from substantially converging to a single probability value.

A method of providing learning capability to an electronic device having a function independent of determining an optimum action, comprising:

• receiving an action performed by a user;
• selecting one of a plurality of processor actions, said action selection being based on an action probability distribution comprising a plurality of probability values corresponding to said plurality of processor actions, wherein said selected processor action affects said electronic device function;
• determining an outcome value based on said user action and said selected processor action;
• and
• updating said action probability distribution based on said outcome value.

A processing device having a function independent of determining an optimum action, comprising:

• an action selection module configured for selecting one of a plurality of processor actions, said action selection being based on an action probability distribution comprising a plurality of probability values corresponding to said plurality of processor actions, wherein said selected processor action affects said electronic device function;
• an outcome evaluation module configured for determining an outcome value based on one or both of said user action and said selected processor action;
• and
• a probability update module configured for updating said action probability distribution based on said outcome value.

A method of providing learning capability to a processing device having one or more objectives, comprising:

• receiving actions from a plurality of users;
• selecting one or more of a plurality of processor actions based on an action probability distribution comprising a plurality of probability values corresponding to said plurality of processor actions;
• determining one or more outcome values based on one or both of said plurality of user actions and said selected one or more processor actions;
• updating said action probability distribution using one or more learning automatons based on said one or more outcome values;
• and
• modifying one or more subsequent processor action selections, outcome value determinations, and action probability distribution updates based on said one or more objectives.

A method of providing learning capability to a processing device having one or more objectives, comprising:

• receiving actions from users divided amongst a plurality of user sets;
• for each of said user sets:
• selecting one or more of a plurality of processor actions based on an action probability distribution comprising a plurality of probability values corresponding to said plurality of processor actions;
• determining one or more outcome values based on one or more actions from said each user set and said selected one or more processor actions;
• updating said action probability distribution using a learning automaton based on said one or more outcome values;
• and
• modifying one or more subsequent processor action selections, outcome value determinations, and action probability distribution updates based on said one or more objectives.

A method of providing learning capability to a processing device having one or more objectives, comprising:

• receiving a plurality of user actions;
• selecting one or more of a plurality of processor actions based on an action probability distribution comprising a plurality of probability values corresponding to said plurality of processor actions;
• weighting said plurality of user actions;
• determining one or more outcome values based on said selected one or more processor actions and said plurality of weighted user actions;
• and
• updating said action probability distribution based on said outcome value.

A processing device having one or more objectives, comprising:

• an action selection module configured for selecting one or more of a plurality of processor actions based on an action probability distribution comprising a plurality of probability values corresponding to said plurality of processor actions;
• an outcome evaluation module configured for weighting a plurality of received user actions, and for determining one or more outcome values based on said selected one or more processor actions and said plurality of weighted user actions;
• and
• a probability update module configured for updating said action probability distribution based on said outcome value.

A method of providing learning capability to a processing device having one or more objectives, comprising:

• receiving a plurality of user actions;
• selecting one of a plurality of processor actions based on an action probability distribution comprising a plurality of probability values corresponding to said plurality of processor actions;
• determining a success ratio of said selected processor action relative to said plurality of user actions;
• comparing said determined success ratio to a reference success ratio;
• determining an outcome value based on said success ratio comparison;
• and
• updating said action probability distribution based on said outcome value.

A processing device having one or more objectives, comprising:

• an action selection module configured for selecting one of a plurality of processor actions based on an action probability distribution comprising a plurality of probability values corresponding to said plurality of processor actions;
• an outcome evaluation module configured for determining a success ratio of said selected processor action relative to a plurality of user actions, for comparing said determined success ratio to a reference success ratio, and for determining an outcome value based on said success ratio comparison;
• and
• a probability update module configured for updating said action probability distribution based on said outcome value.

A method of providing learning capability to a processing device having one or more objectives, comprising:

• receiving actions from a plurality of users;
• selecting one of a plurality of processor actions based on an action probability distribution comprising a plurality of probability values corresponding to said plurality of processor actions;
• determining if said selected processor action has a relative success level for a majority of said plurality of users;
• determining an outcome value based on said success determination;
• and
• updating said action probability distribution based on said outcome value.

A processing device having one or more objectives, comprising:

• an action selection module configured for selecting one of a plurality of processor actions based on an action probability distribution comprising a plurality of probability values corresponding to said plurality of processor actions;
• an outcome evaluation module configured for determining if said selected processor action has a relative success level for a majority of a plurality of users, and for determining an outcome value based on said success determination;
• and
• a probability update module configured for updating said action probability distribution based on said outcome value.

A method of providing learning capability to a processing device having one or more objectives, comprising:

• receiving one or more user actions;
• selecting one or more of a plurality of processor actions that are respectively linked to a plurality of user parameters, said selection being based on an action probability distribution comprising a plurality of probability values corresponding to said plurality of linked processor actions;
• linking said one or more selected process actions with one or more of said plurality of user parameters;
• determining one or more outcome values based on said one or more linked processor actions and said one or more user actions;
• and
• updating said action probability distribution based on said one or more outcome values.

A processing device having one or more objectives, comprising:

• an action selection module configured for selecting one or more of a plurality of processor actions that are respectively linked to a plurality of user parameters, said selection being based on an action probability distribution comprising a plurality of probability values corresponding to said plurality of linked processor actions;
• an outcome evaluation module configured for linking said one or more selected process actions with one or more of said plurality of user parameters, and for determining one or more outcome values based on said one or more linked processor actions and one or more user actions;
• and
• a probability update module configured for updating said action probability distribution based on said one or more outcome values.

A method of providing learning capability to a phone number calling system having an objective of anticipating called phone numbers, comprising:

• generating a phone list containing at least a plurality of listed phone numbers and a phone number probability distribution comprising a plurality of probability values corresponding to said plurality of listed phone numbers;
• selecting a set of phone numbers from said plurality of listed phone numbers based on said phone number probability distribution;
• generating a performance index indicative of a performance of said phone number calling system relative to said objective;
• and
• modifying said phone number probability distribution based on said performance index.

A method of providing learning capability to a phone number calling system, comprising:

• receiving a plurality of phone numbers;
• maintaining a phone list containing said plurality of phone numbers and a plurality of priority values respectively associated with said plurality of phone numbers;
• selecting a set of phone numbers from said plurality of listed phone numbers based on said plurality of priority values;
• communicating said phone number set to a user.