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!