適性化多代理人網際網路環境資訊偵搜 Collaborative Multiagent Adaptation for Business Environmental Scanning through the Internet 劉瑞瓏 Rey-Long Liu 中華大學資訊管理系 中華民國 92 年 11 月 18 日

2 Outline Introduction –Business Environmental Scanning through the Internet (ES) –User-Centered, Continuous, and Resource-Bounded ES (UCRES): Goal & challenges –Intelligent Multiagent Technology Multiagent Adaptation for UCRES –Overview of AESA –The Scanning Agents –The Controlling Agent Experiment Conclusion

3 Introduction Business Environmental Scanning through the Internet (ES) –A routine activity for collecting information of interest (IOI) from the WWW Information about various entities such as the government, competitors, customers, and partners –Supporting the businesses to respond to the newest status of the environment in a timely manner

4 UCRES: User-centered, Continuous, and Resource-bounded ES –Goal & clallenges Finding IOI continuously –Timeliness (TES): minimizing the average time delay of finding IOI –Completeness (CES): maximizing the percentage of IOI found Controlling the resource consumed –Effectiveness (EES): maximizing the possibility of finding IOI in each inquiry of data Considering the preferences of the user (i.e. adding importance weights to TES, CES, and EES) –Weighted TES –Weighted CES –Weighted EES

5 Related multiagent technology for UCRES –Information gathering agents Aiming to satisfy users' "one-shot" needs, e.g. –Intelligently locating the information with the additional consideration of time/cost/quality tradeoffs, and –Filter out irrelevant information But when and how frequently to scan for IOI? –Information monitoring agents Aiming to monitor a predefined set of targets periodically or adaptively But where to find the IOI to monitor?

6 –Adaptive agents for decision support Aiming to provide tailored information for supporting decision making Adapting to what to monitor by observing the user’s preference and/or problem solving strategies But when and how frequently to scan for IOI? –Multiagent coordination & bidding Aiming to resolve conflicts and build consensus among the agents But how to define a coordination and/or bidding protocol for UCRES?

7 –Learning for multiagent coordination, bidding, and organization Aiming to learn –How the actions affect each other, –What information is required for coordination, –When to trigger agent coordination, –Usage of what the agents are bidding for, –Restructuring of the organization for coordination and collaboration But how to simultaneously adapt to –User’s preference –IOI’s distribution in the Internet –Update behavior of the IOI –Limited amount of resource

8 Multiagent Adaptation for UCRES AESA: Adaptive ES Agents –A scalable society of autonomous agents A set of scanning agents A controlling agent –Basic rules of the agent society Collaborating for satisfying the user’s information needs Sharing the limited amount of resource –Adaptation to … User’s preference IOI’s distribution in the Internet Update behavior of the IOI Limited amount of resource

9 Overview of AESA System Administrator Notification Evaluate Scanning Agent 3 Controlling Agent Terminate Adjust Resource AESA Spawn Scanning Agent 1 Scanning Agent 2 Scanning Agent n Information Preferences Resource Limit User 1 User 2 User m Monitoring Discovery Intranet The WWW Information Space

10 Behavior of each Scanning Agent (1) Goal = Set of information preferences to be satisfied by the agent; (2) Target = Target web site from which the agent is delegated to find IOI; (3) ResourceU = Upper bound of resource consumption by the agent; Repeat (4) If the agent is ready to retrieve information (according to ResourceU), (4.1) Retrieve information from Target; (4.2) If new information is found, (4.2.1) Issue a Type-E request to get (DegreeOfSatisfaction, SatisfiedGoal); (4.2.2) Aliveness = Aliveness *  + DegreeOfSatisfaction; (4.2.3) Trigger suitable procedures, including event logging and user notification; (4.2.4) If Aliveness  , for each target d found in the new information, ( ) Issue a Type-S request to spawn a new agent (4.3) If the agent succeeds two times, issue a Type-R-More request for more resource; (4.4) If the agent fails two times, issue a Type-R-Less request for releasing resource; (5) If an information preference is no longer valid, remove the need from Goal; Until Aliveness   or Goal is empty; (6) Terminate the agent by issuing a Type-T request.

11 Behavior of the Controlling Agent (1)  = Upper bound of the resource that may be consumed by the system; Repeat (2) If a user enters a new preference e, generate an agent (if necessary and possible); (3) If a user removes an information preference e, inform all agents to remove e; (4) If there is a Type-R-Less request from agent k, (4.1) Release (from k)  1 percents of resource; (5) If there a Type-R-More request from agent k, (5.1) Allocate (to k)  2 percents of additional resource (by agent aliveness); (6) If there is a Type-E request from agent k, (6.1) Reply k with DegreeOfSatisfaction =  (DOS e *Importance e ), for each goal e; (7) If there is a Type-S request to spawn a new agent, (7.1) Reply the requesting agent with  (if possible); (8) If there is a Type-T request from agent k, terminate k and release all resource of k; (9) If there is no response from k for a long time, terminate k and release all resource of k; Until the system is terminated.

12 Experiment Three dimensions of the update behavior of a web site w whose central category is c: (1) When to update: following exponential update The probability of updating w in time interval x follows the probability density function f(x)= e - x, where is the average update frequency of w. (2) Which part to update: (2a) Main content is updated with a probability of 0.5. (2b) Embedded hyperlinks are updated with a probability of 0.5. (3) Way to update: preserving information relatedness (3a) New main content is still of category c. (3b) Let s be the semantic difference between a new hyperlink and c: (3b1) 60% new hyperlinks link to those having s  1. (3b2) 30% new hyperlinks link to those having 2  s  3. (3b3) 10% new hyperlinks link to those having s  4. Simulating the dynamic information space

13 Simulating users’ dynamic preferences The importance of an information preference may be dynamic due to various factors in business administration (e.g. evolutions of internal strategic goals, sales seasons) and external environmental events Among the 100 possible categories of interest, 10 categories were randomly selected as users' information preferences to be satisfied. Each preference k was initially associated with a random importance level I k (0 < I k  1), which could be dynamically changed. The timing of changing I k followed the exponential probability density function f(x) =  e -  x as well, where  was the average frequency of changing I k. For each preference k,  randomly ranged from 1/3000 to 1/2000 (time/seconds).

14 The systems evaluated –AESA Basic setting –For the controlling agent: »  1 =  2 =20, »  = 1 inquiry per second, »  = 1 inquiry per 200 seconds Variants –AESA-1 »For the scanning agents,  =0.5,  =0.5, and  =0.3 –AESA-2 »For the scanning agents,  =0.6,  =0.6, and  =0.3 –Recall that,  is related to agent cloning,  is related to agent aliveness evaluation, and  is related to agent termination.  AESA-1 has a more dynamic agent society than AESA-2 (the agents are easier to be generated and terminated)

15 –BestFirstD Aiming to represent the way of employing best-first discovery to UCRES It was allowed to continuously traverse through the information space (rather than performing "one-shot" traversal) It preferred the most promising hyperlinks when traversing the information space To avoid duplicated traversals, if there were multiple attempts trying to traverse to a web site w for a user interest, only one traversal was conducted Variants –BestFirstD-1 and BestFirstD-2, which selected those hyperlinks whose semantic differences with the user interest were less than or equal to 1 and 2, respectively. –Obviously BestFirstD-2 could traverse a larger space than BestFirstD-1.

16 –BestDPeriodicalM Aiming to represent the way of integrating information discovery with information monitoring It integrated best-first discovery (i.e. BestFirstD) with periodical monitoring, which was the most popular monitoring technique in environmental scanning –Once a web site is discovered, BestDPeriodicalM determined a fixed monitoring frequency randomly ranging from 1/10 to 1/1000 query/seconds (this was a “mercy” to BestDPeriodicalM) Variants –BestDPeriodicalM-1 and BestDPeriodicalM-2, which selected those hyperlinks whose semantic differences with the user interest were less than or equal to 1 and 2, respectively –Obviously BestDPeriodicalM-2 could traverse a larger space than BestDPeriodicalM-1.

17 Results (average of 30 runs)

18 Results (average of 30 runs)

19 Improvements TES: 191% WTES: 252% EES: 1220% WEES: 1586% CES: 1157% WCES: 1413%

20 Conclusion Finding more IOI in a timelier manner using less resource Collaborative Multiagent Adaptation –Multiagent: Scalable ES –Collaborative: Sharable resource and findings –Adaptation: Adjustable ES for dynamic Information spaces (distribution & update behaviors of IOI), User preferences, and Resource upper bounds