SYSTEM FOR SEQUENCING AND PLANNING

1 .- 41 . (canceled)

42 . A machine-learning model-based combination chunker/planner system, the chunker/planner system comprising:

i. a machine learning component (“Sequencer”) configured for receiving sequential input, for dividing the sequential input into one or more chunks, and for generating a plan corresponding to each chunk; and

ii. a second machine learning component (“Planner”) configured for pursuing a reward, for selecting from plans generated by the Sequencer those plans most closely associated with reaping the reward in a current state, and for activating a selected plan.

43 . The chunker/planner system of claim 42 wherein the Sequencer is further configured for dividing the sequential input based on an element selected from the group consisting of: receiving an explicit end-of-sequence input, reaching a maximum size for a current chunk, receiving a reward to associate with a current chunk, and receiving an input whose value differs from expected values by more than a set threshold.

44 . The chunker/planner system of claim 42 wherein generating a plan corresponding to a chunk comprises:

i. generating a declarative representation (“tonic”) associated with the entire chunk; and

ii. as each element of the chunk is examined in input sequence:

1. querying the Planner for a complete plan consistent with the chunk as examined so far; and

2. using a complete plan returned by the Planner, the tonic, a time-decaying context, and a most recently examined element in the chunk to predict a next element in the chunk.

45 . The chunker/planner system of claim 42 :

i. wherein the Planner is further configured for associating with a plan a change of state produced when the plan is activated; and

ii. wherein the Planner is further configured for pursuing a goal state, for selecting from plans generated by the Sequencer those plans most closely associated with accomplishing a change of state from a current state to a state closer to the goal state, and for activating a selected plan.

46 . The chunker/planner system of claim 42 further comprising an input buffer for the Sequencer; wherein the Sequencer is further configured to:

i. receive the sequential input into the input buffer;

ii. respond to a user command to discard the contents of the input buffer; and

iii. respond to a user command to train the Sequencer to turn the contents of the input buffer into a plan and to record it as a chunk.

47 . The chunker/planner system of claim 42 further configured to:

i. receive a partial input;

ii. select a best match among existing plans that are consistent with the partial input;

iii. predict a result and reward from activating the selected plan; and

iv. activate the selected plan.

48 . The chunker/planner system of claim 42 further configured to:

i. receive a partial input;

ii. infer a probability distribution of existing plans consistent with the partial input;

iii. predict a probability distribution of results and rewards from activating the consistent plans; and

iv. predict a next element in the input, the prediction based, at least in part, on the probability distribution.

49 . The chunker/planner system of claim 42 wherein the Sequencer is a self organizing map.

50 . The chunker/planner system of claim 42 wherein the Planner is a self organizing map.

51 . In a computer-implemented system, a method for directing behaviour, the method comprising:

i. receiving, by a first machine learning component (“Sequencer”), sequential input;

ii. dividing the sequential input into one or more chunks;

iii. generating a plan corresponding to each chunk; and

iv. pursuing, by a second machine learning component (“Planner”), a reward, wherein pursuing a reward comprises selecting from plans generated by the Sequencer those plans most closely associated with reaping the reward in a current state and activating a selected plan.

52 . The method for directing behaviour of claim 51 further comprising:

i. associating, by the Sequencer, with a plan a change of state produced when the plan is activated; and

ii. pursuing, by the Planner, a goal state, wherein pursuing a goal state comprises selecting from plans generated by the Sequencer those plans most closely associated with accomplishing a change of state from a current state to a state closer to the goal state and activating a selected plan.

53 . A system for controlling an application, the system comprising:

i. a machine-learning model-based combination chunker/planner system comprising:

a first machine learning component (“Sequencer”) configured for receiving sequential input from the application, for dividing the sequential input into one or more chunks, and for generating a plan corresponding to each chunk; and

a second machine learning component (“Planner”) configured for pursuing a reward, for selecting from plans generated by the Sequencer those plans most closely associated with reaping the reward in a current state, and for activating a selected plan by communicating with the application;

ii. wherein the Sequencer is further configured for associating with a plan a change of state produced when the plan is activated; and

iii. wherein the Planner is further configured for pursuing a goal state, for selecting from plans generated by the Sequencer those plans most closely associated with accomplishing a change of state from a current state to a state closer to the goal state, and for activating a selected plan by communicating with the application.

54 . The system of claim 53 wherein the controlled application is selected from the group consisting of: an industrial process, a manufacturing process, an online planning/collaboration application, and an online service avatar.

55 . The system of claim 53 wherein the Sequencer is a self organizing map.

56 . The system of claim 53 wherein the Planner is a self organizing map.

57 . A machine-learning model-based system configured for detecting frequently occurring subsequences in sequential input and representing the subsequences as a whole, the system comprising: a neural-network self-organizing map (“Sequencer”) configured for receiving the sequential input, for dividing the sequential input into one or more subsequences, and for generating a plan corresponding to each subsequence.

58 . The system of claim 57 wherein the Sequencer is further configured for dividing the sequential input based on an element selected from the group consisting of: receiving an explicit end-of-sequence input, and receiving an input whose value differs from expected values by more than a set threshold.

59 . The system of claim 57 wherein generating a plan corresponding to a chunk comprises:

generating a declarative representation (“tonic”) associated with the entire chunk; and

as each element of the chunk is examined in input sequence:

using the tonic, a time-decaying context, and a most recently examined element in the chunk to predict a next element in the chunk.

60 . The system of claim 57 further configured to:

receive as a partial input a fragment of a sequence;

select a best match among existing plans that are consistent with the partial input;

predict a likely next element in the sequential input from activating the selected plan; and

activate the selected plan.

61 . The system of claim 57 further configured to:

receive as a partial input a fragment of a sequence;

infer a probability distribution of existing plans consistent with the partial input;

predict a probability distribution of likely next elements in the sequential input from activating the consistent plans; and

predict a next element in the input, the prediction based, at least in part, on the probability distribution.