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Towards a Pattern Recognition Approach for Transferring Knowledge in ACM
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Towards a Pattern Recognition Approach for Transferring
Knowledge in ACM
Thanh Tran Thi Kim
Christoph Ruhsam
Max J. Pucher ISIS Papyrus Europe AG,
Austria
Maximilian Kobler University of Applied Sciences
Burgenland, Autria
Jan Mendling Vienna University of
Economics and Business, Institute for Information
Business, Austria
Overview
Motivation
ACM fundamental concepts
User-Trained Agent (UTA) principles
Applying the UTA in ACM
Benefits of the approach
Motivation
ACM helps KWs to deal with unpredictable situations.
Support KWs with context-sensitive proposals instead of extensive prescriptive procedures.
How to support knowledge acquisition, sharing and collaboration by ACM?
Peter Drucker
“Knowledge is only between two ears”
The User Trained Agent (UTA)
Capture knowledge by observing KWs activities!
Real-time transductive learning through observations during normal user interaction.
No training needed!
Knowledge is stored in a central knowledge base.
Share knowledge between individuals, groups, departments and across locations.
Propose best next actions to influence future ACM execution.
A learning organization needs a learning system.
Contract Contract
Proposal
Content
Customer Database
Entities
Goals
Authorization Policy
Rules
Library
Tasks
User Interaction
Cases
ACM Fundamental Concepts
UTA Principles
UTA is built on pattern recognition principles.
Instant activity observations through behavioural data patterns
UTA observes state patterns of a Case:
Goals, Data artefacts, Tasks and Processes,
Rules, etc.
Learning related to the role of the performer.
UTA in ACM
Design time Run time
Adapting phase
ACM
Observe
Transfer Knowledge
Suggest
UTA
Knowledge worker
Knowledge Handling of UTA
The internal knowledge of UTA consists of:
Learning Samples Container
Action Container
Feature Container
Cluster Container
Learning Samples Container
Collection of samples observed by the UTA whenever a knowledge worker executed an ad hoc action.
Learning Sample Properties
Input data: all relevant data which describe the state of a Case.
Pointer to the learned action.
Indicates whether it is a positive or negative sample.
Indicate whether the sample was learned from KWs or automatically by the UTA (implicit negative samples).
UTA Learning Modes
Positive learning
Perform matching actions (“positive”).
More samples needed to find differences.
Negative learning is needed.
Explicit negative learning
Declare samples explicitly as “negative”.
Implicit negative learning
Assume that samples for a certain action are negative samples for other actions
Action Container
Contains all learned actions
Updated when a previously unknown action is observed.
All information about used parameters is captured.
Feature Container
Collects all available features (=object data attributes) observed for a certain action
Cluster Container
Decision cluster: map between observed actions and relevant features
UTA Main Functions
1. Learn user actions related to case patterns. Triggered in real-time by changes in the defined state space of the Case.
2. Recommend actions when similar patterns are identified Role of performer is considered
KWs can decide whether to follow the recommendation or execute another ad hoc action.
State Space Scopes
Theoretically all data attributes of a certain Case can be observed
Contains a lot of „noise“
Business Ontologies map business objects with the underlying ACM object model
Filter only for business relevant items
Faster learning
UTA Recommendations
Current sample is compared against the knowledge base in respect to the relevant features.
Good match: sample already exists in the knowledge base, confidence will be increased.
Confidence rated from 1 (low) – 5 (high).
No match: Sample added to the knowledge base for evaluation of feature relevance.
Knowledge can stem from diverse business situations of a company, a certain department or only specific case types.
Confidence Calculation
Sample
Sample
Feature evaluation
0 Action Quality
UTA Test Case: Contract Management
Standard covered by a predefined Case Template.
KW finds out that an exception handling is needed for a contract value within a certain range.
E.g. perform additional checks before approving when the value is 500.000 – 700.000 EUR.
Check transfer fees, transfer conditions between the banks
These activities and the range were not foreseen in the Case Template.
KW defines an ad hoc Task.
UTA learns and supports others with BNAs in similar situations.
Influence of Ontology
Using ontology, the UTA observations are filtered to contain only the relevant business data.
The confidence rating reaches quickly high ratings.
Without ontology the confidence raises slower and takes longer until a stable state is reached.
Influence of Negative Learning
Ontology is applied
Without negative learning the confidence stays constant at 2 stars.
With negative learning, the suggestions quickly reaches high confidence.
Best Next Action User Interactions
Accept a suggestion: Related action is passed to the UTA as positive learning sample.
Reject a suggestion: Related action is passed to the UTA as explicitly negative learning sample.
No selection will not influence the knowledge base
Benefits of the UTA Approach
The UTA observes user actions and transfers the acquired knowledge from single KWs to teams.
Continuous knowledge acquisition and sharing.
The UTA‘s knowledge is gradually built by learning during normal work with the ACM system.
No extra training by specialists is needed!
Negative learning samples are important!
The rating of observed situations is maintained by the UTA throughout the life-time of the system with full transparency to ACM Users.
The recommendations from the UTA are objective and increase in confidence accordingly.
Thank you for your attention!