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:

• identifying a move performed by said game player;
• selecting one of a plurality of game moves based on a game move probability distribution comprising a plurality of probability values corresponding to said plurality of game moves;
• determining an outcome of said selected game move relative to said identified player move;
• updating said game move probability distribution based on said outcome;
• and
• modifying one or more of said game move selection, said outcome determination, and said game move probability distribution update 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 a game move probability distribution comprising a plurality of probability values organized among a plurality of game move subsets, said plurality of probability values corresponding to a plurality of game moves;
• selecting one of said plurality of game move subsets;
• and
• selecting one of said plurality of game moves from said selected game move 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 a game move probability distribution using one or more learning algorithms, said game move probability distribution comprising a plurality of probability values corresponding to a plurality of game moves;
• modifying said one or more learning algorithms;
• and
• updating said game move 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:

• identifying a move performed by said game player;
• selecting one of a plurality of game moves based on a game move probability distribution comprising a plurality of probability values corresponding to said plurality of game moves;
• determining if said selected game move is successful;
• determining a current skill level of said game player relative to a current skill level of said computer game;
• and
• updating said game move probability distribution using a reward algorithm if said selected game move is successful and said relative skill level is relatively high, or if said selected game move 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:

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

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

• identifying a move performed by said game player;
• selecting one of a plurality of game moves based on a game move probability distribution comprising a plurality of probability values corresponding to said plurality of game moves;
• determining if said selected game move 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 if said selected game move is successful and said relative skill level is relatively high, or if said selected game move is unsuccessful and said relative skill level is relatively low;
• and
• updating said game move probability distribution based on said successful outcome.

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

• identifying a move performed by said game player;
• selecting one of a plurality of game moves based on a game move probability distribution comprising a plurality of probability values corresponding to said plurality of game moves;
• determining if said selected game move 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 if said selected game move is unsuccessful and said relative skill level is relatively high, or if said selected game move is successful and said relative skill level is relatively low;
• and
• updating said game move probability distribution based on said unsuccessful outcome.

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

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

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

• identifying 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 of one or both of said identified user action and said selected processor action;
• updating said action probability distribution based on said outcome;
• and
• wherein said action probability distribution is prevented from substantially converging to a single probability value.

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

• identifying 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 processing device function;
• determining an outcome of said selected processor action relative to said identified user action;
• and
• updating said action probability distribution based on said outcome.

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 processing device function;
• an outcome evaluation module configured for determining an outcome of one or both of said identified user action and said selected processor action;
• and
• a probability update module configured for updating said action probability distribution based on said outcome.

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

• identifying 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 outcomes of one or both of said identified 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 outcomes;
• and
• modifying one or more of said processor action selection, said outcome determination, and said action probability distribution update based on said one or more objectives.

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

• identifying 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 outcomes of 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 outcomes;
• and
• modifying one or more of said processor action selection, said outcome determination, and said action probability distribution update based on said one or more objectives.

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

• identifying 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 identified plurality of user actions;
• determining one or more outcomes of said plurality of weighted user actions;
• and
• updating said action probability distribution based on said one or more outcomes.

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 identified user actions, and for determining one or more outcomes of said plurality of weighted user actions;
• and
• a probability update module configured for updating said action probability distribution based on said outcome.

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

• identifying 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 identified plurality of user actions;
• comparing said determined success ratio to a reference success ratio;
• determining an outcome of said success ratio comparison;
• and
• updating said action probability distribution based on said outcome.

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 of said success ratio comparison;
• and
• a probability update module configured for updating said action probability distribution based on said outcome.

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

• identifying 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 of said success determination;
• and
• updating said action probability distribution based on said outcome.

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 of said success determination;
• and
• a probability update module configured for updating said action probability distribution based on said outcome.

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

• 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 outcomes of said one or more linked processor actions;
• and
• updating said action probability distribution based on said one or more outcomes.

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 outcomes of 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 outcomes.

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

• generating a list containing a plurality of listed items with an associated item probability distribution comprising a plurality of probability values corresponding to said plurality of listed items;
• selecting one or more items from said plurality of listed items based on said item probability distribution;
• determining a performance index indicative of a performance of said processing device relative to said objective;
• and
• modifying said item probability distribution based on said performance index.

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

• generating a plurality of lists respectively corresponding to a plurality of item parameter values, each of said plurality of lists containing a plurality of listed items with an associated item probability distribution comprising a plurality of probability values corresponding to said plurality of listed items;
• selecting a list corresponding to a parameter value exhibited by a currently identified action associated item;
• and
• in said selected list, selecting one or more listed items from said plurality of listed items based on said item probability distribution;
• determining a performance index indicative of a performance of said processing device relative to said objective;
• and
• modifying said item probability distribution based on said performance index.

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;
• determining 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:

• identifying a plurality of phone numbers associated with a plurality of phone calls;
• 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.

A method of providing learning capability to a television channel control system having an objective of anticipating watched television channels, comprising:

• generating a list containing a plurality of listed television channels with an associated television channel probability distribution comprising a plurality of probability values corresponding to said plurality of listed television channels;
• selecting one or more television channels from said plurality of listed television channels based on said television channel probability distribution;
• determining a performance index indicative of a performance of said processing device relative to said objective;
• and
• modifying said television channel probability distribution based on said performance index.

A method of providing learning capability to a television channel control system, comprising:

• generating a plurality of lists respectively associated with a plurality of television channel parameter values, each of said plurality of lists containing a plurality of listed television channels with an associated television channel probability distribution comprising a plurality of probability values corresponding to said plurality of listed television channels;
• selecting a list corresponding to a television channel parameter value exhibited by a currently watched television channel;
• and
• in said selected list, selecting one or more listed actions from said plurality of listed actions based on said action probability distribution;
• determining a performance index indicative of a performance of said processing device relative to said objective;
• and
• modifying said action probability distribution based on said performance index.

A method of providing learning capability to an educational toy, comprising:

• selecting one of a plurality of toy actions based on an action probability distribution comprising a plurality of probability values corresponding to said plurality of toy actions, said plurality of toy actions being associated with a plurality of different difficulty levels;
• identifying an action performed by a user;
• determining an outcome of said selected toy action relative to said identified user action;
• and
• updating said action probability distribution based on said outcome and said difficulty level of said selected toy action.

A method of providing learning capability to an educational toy having an objective of increasing an educational level of a user, comprising:

• selecting one of a plurality of toy actions based on an action probability distribution comprising a plurality of probability values corresponding to said plurality of toy actions, said plurality of toy actions being associated with a plurality of different difficulty levels;
• identifying an action performed by said user;
• determining an outcome of said selected toy action relative to said identified user action;
• updating said action probability distribution based on said outcome;
• and
• modifying one or more of said toy action selection, outcome determination, and action probability distribution update based on said objective.

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

• 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, said plurality of processor actions being associated with a plurality of different difficulty levels;
• identifying an action performed by a user;
• determining an outcome of said selected processor action relative to said identified user action;
• and
• updating said action probability distribution based on said outcome and said difficulty level of said selected processor action.

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

• 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, said plurality of processor actions being associated with a plurality of different difficulty levels;
• identifying an action performed by said user;
• determining an outcome of said selected processor action relative to said identified user action;
• updating said action probability distribution based on said outcome;
• and
• modifying one or more of said action selection, outcome determination, and action probability distribution update based on said objective.

A processing device having one or more objectives, 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, said plurality of processor actions being associated with a plurality of different difficulty levels;
• an outcome evaluation module configured for determining an outcome of said selected processor action relative to said user action;
• and
• a probability update module configured for updating said action probability distribution based on said outcome and said difficulty level of said selected processor action.