Autonomous learning and reasoning agent

• US 5,586,218 A
• Filed: 08/24/1995
• Issued: 12/17/1996
• Est. Priority Date: 03/04/1991
• Status: Expired due to Fees

• Current Assignee: eGain Communications Corporation
• Original Assignee: Inference Corporation
• Inventors: Allen, Bradley P.
• Primary Examiner(s): Downs, Robert W.
• Application Number:
  US08/518,850
• Time in Patent Office: 481 Days
• Field of Search: 395/10, 395/50, 395/51, 395/75, 395/77, 395/11, 395/13, 395/600, 395/934
• US Class Current: 706/12
• CPC Class Codes: G06N 3/004 Artificial life, i.e. computing arrangements simulating life
• CPC Class Codes: G06N 3/126 Evolutionary algorithms, e.g. genetic algorithms or genetic programming
• CPC Class Codes: G06N 5/04 Inference or reasoning models

Abstract

A software agent which performs autonomous learning in a real-world environment, implemented in a case-based reasoning system and coupled to a sensor for gathering information from, and to an effector for manipulating, its environment. A case base which is tuned in response to an evaluation of how well the agent is operating in that environment. A memory of cases, the contents of that memory being determined by a genetic technique, including producing new cases which may never have been encountered in the environment, evaluating cases in response to a history of previous matches and in response to an external stimulus, and selecting a limited set of cases which provides a preferred model of the environment. A selection technique based on multiple measures, such as match quality, case accuracy, or case utility, which may induce experimentation by the autonomous agent, such as by employing a random or pseudorandom effect in selecting cases, and which may also distinguish between those actions which solve problems and those actions which gather further information so as to better solve problems. Multiple autonomous agents may form a collective entity and may cooperate to select an action to be performed by that collective entity.

Claims

1. An autonomous software agent for operating in an environment, comprising

a sensor for gathering information from said environment;

an effector for manipulating said environment;

a case base having a plurality of exemplar cases, each of said exemplar cases comprising a designated action, at least some of said actions comprising gathering information;

an inference engine for performing case-based reasoning steps in response to said cases; and

means for altering said case base in response to reinforcement from said environment;

means for selecting a set of matching cases from said case base having a random effect or a pseudorandom effect;

wherein said means for selecting a set of matching cases from said case base comprises means for selecting a first case over a second case with a likelihood that is linearly proportional to a ratio of a measure of case accuracy of said first case over said second case.

2. An autonomous software agent as in claim 1, wherein said means for altering comprises means for altering said case base in response to an evaluation of said agent.

3. An autonomous software agent as in claim 1, comprising

wherein said case base comprises a memory of cases, at least part of said memory being determined by a genetic technique;

and

wherein said case-based reasoning steps comprise taking selected actions in response to said cases, and at least some of said actions comprise gathering information.

4. An autonomous software agent as in claim 3, wherein said means for altering comprises means for altering said case base in response to an evaluation of said agent.

5. An autonomous software agent as in claim 3, wherein said means for selecting a case for case-based reasoning is responsive to a plurality of measures.

6. An autonomous software agent as in claim 5, wherein said means for selecting comprises means for selecting a case in response to match quality, case accuracy, or case utility.

7. An autonomous software agent as in claim 3, wherein said means for selecting a case for case-based reasoning comprises means for inducing experimentation by the autonomous agent.

8. An autonomous software agent as in claim 7, wherein said means for selecting a case comprises means for generating a random or pseudorandom effect.

9. An autonomous software agent as in claim 8, wherein said means for selecting a case comprises means for inducing experimentation by said agent.

10. An autonomous software agent as in claim 3,

comprising means for cooperating with a second autonomous software agent in selecting an action to be performed.

11. An autonomous software agent as in claim 1, comprising

a receptor coupled to said environment and generating a features message;

wherein said inference engine is coupled to said receptor, said inference engine comprising said case base, said means for selecting a set of matching cases from said case base, at least one case in said case base comprising a designated action including a step of gathering information, means for altering said case base using a genetic technique in response to said features message and in response to a reinforcement from said environment, and means for generating a commands message in response to said set of matching cases; and

wherein said effector is coupled to said environment and operates on said environment in response to said commands message.

12. An autonomous software agent as in claim 11, wherein said means for altering comprises

means for generating a case which has not been encountered in said environment;

means for evaluating a case in response to (a) a set of matching cases, or (b) said reinforcement; and

means for selecting a limited set of cases which provides a preferred model of said environment.

13. An autonomous software agent as in claim 1, comprising

a receptor coupled to said environment and generating a features message;

wherein said inference engine is coupled to said receptor, said inference engine comprising said case base, said means for selecting a set of matching cases from said case base, means for altering said case base using a genetic technique in response to said features message and in response to a reinforcement from said environment, and means for generating a commands message in response to said set of matching cases; and

wherein said effector is coupled to said environment and operates on said environment in response to said commands message;

wherein said means for selecting a set of matching cases from said case base comprises means for generating a random effect or a pseudorandom effect.

14. An autonomous software agent as in claim 1, wherein said means for selecting a set of matching cases from said case base comprises means for applying a random effect or a pseudorandom effect to a measure of match quality, case accuracy, or case utility.

15. An autonomous software agent as in claim 1, wherein said means for selecting a set of matching cases from said case base is more likely to select a case with a greater measure of match quality, case accuracy, or case utility.

16. An autonomous software agent as in claim 1, wherein substantially all of said cases comprise

a set of matchable features;

an action to be taken when said case is selected; and

a plurality of measures of value for said case.

17. An autonomous software agent as in claim 16, wherein at least one of said plurality of measures of value comprises match quality, case accuracy, or case utility.

18. An autonomous software agent as in claim 1, wherein said environment comprises a carpenter'"'"'s bench, a chess program, a customer service system, a graphic database, a help-desk system, a knowledge discovery system, a loan portfolio database, a music audio database, an oil refinery, a piece of office equipment, or a physical room.

19. An autonomous software agent as in claim 1, comprising

a receptor coupled to said environment and generating a features message;

wherein said inference engine is coupled to said receptor, said inference engine comprising said case base, said means for selecting a set of matching cases from said case base in response to a plurality of measures of case quality, means for altering said case base using a genetic technique in response to said features message and in response to a reinforcement from said environment, and means for generating a commands message in response to said set of matching cases; and

wherein said effector is coupled to said environment and operates on said environment in response to said commands message.

20. An autonomous software agent as in claim 1

wherein each of said exemplar cases comprises a plurality of measures of case quality.