Method and apparatus for identifying an agent running on a device in a computer network

• US 5,619,615 A
• Filed: 07/22/1994
• Issued: 04/08/1997
• Est. Priority Date: 07/22/1994
• Status: Expired due to Term

• Current Assignee: Avaya Holdings Ltd (Avaya Incorporated)
• Original Assignee: Bay Networks, Inc. (Nortel Networks Corporation)
• Inventors: Pitchaikani, Balaji, Luo, Chen-Yea
• Primary Examiner(s): Downs, Robert W.
• Application Number:
  US08/279,491
• Time in Patent Office: 991 Days
• Field of Search: 395/51, 395/700, 395/600, 395/11, 395/200.01, 395/200.03, 395/200.11
• US Class Current: 706/10
• CPC Class Codes: G06F 16/10 File systems; File servers

Abstract

A method and apparatus for identifying an agent based on a decision tree is provided. The apparatus transmits a series of queries to the agent and the agent transmits responses to the queries back to the apparatus. Based on the responses, the apparatus determines the identity of the agent. The apparatus determines the series of queries by traversing the decision tree based on responses sent by the agent to previous queries. When the traversal arrives at a leaf node of the decision tree, an agent associated with the leaf node is used to identify the agent. If the traversal fails before arriving at a leaf node, then a weight is determined for each supported agent based on the responses sent by the agent, and the agent with the greatest weight is used to identify the agent.

Claims

1. A method for identifying an agent running on a device in a network system, the method comprising the steps of:

a) a network management station (NMS) establishing a plurality of agents as a set of possible agents;

b) said NMS transmitting a current query over said network to said agent, said current query requesting information regarding one or more characteristics of said agent;

c) said NMS receiving a current response over said network from said agent;

d) determining a set of likely agents based on said current response, said set of likely agents being a subset of said set of possible agents;

e) if said set of likely agents includes more than one agent, then said NMS;

i) selecting a new query based upon said current response,

ii) establishing said new query as said current query,

iii) establishing said set of likely agents as said possible set of agents,

iv) repeating steps (b) through (e) until said set of likely agents includes less than two agents; and

f) if said set of likely agents includes a single agent, then said NMS selecting said single agent to identify said agent.

2. The method of claim 1 further including the step of, if said set of likely agents includes no agents, then selecting a substitute agent of said plurality of agents to identify said agent, said substitute agent being selected based upon each said current response received from said agent.

3. The method of claim 2 wherein said step of selecting said substitute agent includes the steps of:

determining a weight for each agent of said plurality of agents, said weight of a given agent being based upon a comparison between a plurality of capabilities supported by said agent and a plurality of capabilities supported by said given agent; and

selecting an agent with a largest weight as said substitute agent.

4. The method of claim 1 wherein said step of establishing said plurality of agents as said set of possible agents comprises a step of establishing a root node of a decision tree as a current node, said decision tree including a plurality of nodes connected by a plurality of branches, each node of said plurality of nodes being associated with an agent, said set of possible agents consisting of an agent associated with said current node and the agents associated with all nodes on said decision tree below said current node.

5. The method of claim 4 wherein said step of determining said set of likely agents based on said current response includes the step of determining a matching branch based on said current response, said matching branch connecting said current node to a child node, said set of likely agents consisting of an agent associated with said child node and the agents associated with all nodes below said child node in said decision tree.

6. The method of claim 5 wherein each branch in said decision tree is associated with a response group, said step of determining a matching branch originating at said current node based on said current response including the steps of determining a response group which includes response values which match said current response, and selecting the branch associated with said response group as said matching branch.

7. The method of claim 4 further comprising the step of constructing said decision tree based on a script file.

8. The method of claim 7 wherein said script file contains human-readable entries.

9. The method of claim 8 wherein each entry includes data indicative of a specified agent and data indicative of a parent agent, said step of constructing said decision tree based on said script file including the steps of creating a new node, associating said new node with said specified agent, and linking said new node to a parent node associated with said parent agent with a new branch.

10. The method of claim 9 wherein each entry further includes data indicative of a response group, said step of constructing said decision tree based on said script file further including the step of associating said response group with said new branch.

11. The method of claim 1 wherein at least one of said plurality of agents is a virtual agent, said virtual agent representing a hypothetical agent capable of supplying predetermined types of information.

12. A method for identifying an agent running on a device in a network, the method comprising the steps of:

A. constructing a decision tree by performing the steps of

(1) reading a script file;

(2) providing a plurality of nodes, said plurality of nodes including a root node and a plurality of leaf nodes;

(3) associating an agent to each node of said plurality of nodes;

(4) associating a query with each node of said plurality of nodes except said plurality of leaf nodes;

(5) providing a plurality of branches, each branch of said plurality of branches originating in an originating node of said plurality of nodes and terminating in a terminating node of said plurality of nodes;

(6) establishing a response group for each branch of said plurality of branches, said response group for a given branch of said plurality of branches including at least one possible response to said query associated with said originating node;

B. identifying said agent based upon said decision tree by performing the steps of

(1) establishing said root node as a current node, said current node being said originating node for a plurality of current branches;

(2) establishing said query associated with said current node as a current query;

(3) transmitting said current query to said agent over said network;

(4) receiving said a current response to said current query from said agent over said network;

(5) comparing said current response to said response groups associated with said current branches;

(a) if said current response belongs to a given response group associated with a given current branch and said terminating node of said given current branch is a leaf node, then selecting said agent associated with said terminating node to identify said agent; and

(b) if said current response belongs to a given response group associated with a given current branch and said terminating node of said given current branch is not a leaf node, then establishing said terminating node as said current node and repeating steps (B)(2) to (B)(5).

13. An apparatus for identifying an agent running on a device in a network, the apparatus comprising:

a network access unit connected to the device over the network;

a storage unit for storing a plurality of instructions, for storing a decision tree and for storing a database, said database including data indicative of a plurality of agents; and

a processor coupled to the network access unit and the storage unit, the processor being configured to read and execute said plurality of instructions from said storage unit;

said plurality of instructions including instructions for;

a) establishing a root node of said decision tree as a current node;

b) causing said network access unit to transmit a current query over said network to said agent, said current query being a query associated with said current node, said current query requesting information regarding one or more characteristics of said agent;

c) receiving a current response over said network from said agent through said network access unit;

d) comparing said current response to a plurality of current response groups to determine a matching branch, each current response group of said plurality of current response groups being a response group associated with a branch originating at said current node, said matching branch being a branch associated with a current response group which matches said current response, said matching branch having a terminating node;

e) identifying said agent associated with said terminating node if said terminating node is a leaf node; and

f) establishing said terminating node as said current node and repeating steps (a) to (e) if said terminating node is not a leaf node.

14. The apparatus of claim 13 wherein said plurality of instructions further includes instructions for selecting a substitute agent of said plurality of agents to identify said agent if said current response does not belong to any said current response groups.

15. The apparatus of claim 14 wherein said each current response sent by said agent indicates capabilities of said agent, said instructions for selecting said substitute agent of said plurality of agents to identify said agent including instructions for:

determining a weight for each agent of said plurality of agents, said weight of a given agent being based upon a comparison between said capabilities of said agent and capabilities supported by said given agent; and

selecting an agent with a largest weight as said substitute agent.

16. The apparatus of claim 13 wherein said storage unit is configured to store a script file, said plurality of instructions further comprising instructions for constructing said decision tree based on said script file.

17. The apparatus of claim 16 wherein said script file contains human readable entries.

18. The apparatus of claim 17 wherein each entry of said script file includes data indicative of a specified agent and data indicative of a parent agent, said instructions for constructing said decision tree based on said script file including instructions for creating a new node, associating said new node with said specified agent, and linking said new node to a parent node associated with said parent agent with a new branch.

19. The apparatus of claim 18 wherein each entry further includes data indicative of a response group, said instructions for constructing said decision tree based on said script file further including instructions for associating said response group with said new branch.

20. The apparatus of claim 13 wherein at least one of said plurality of agents is a virtual agent, said virtual agent representing a hypothetical agent capable of supplying predetermined types of information.