# Combining DMN and the Knowledge Base Paradigm for Flexible Decision Enactment

Ingmar Dasseville, Laurent Janssens,

Gerda Janssens, Jan Vanthienen,Marc Denecker

http://krr.bitbucket.org/autoconfig

# DMN

Decision Requirements Diagram

Input

Output

U At Risk Model Convertible Price Potential Theft Rating
Boolean Boolean Currency (\$) {High, Moderate,Low}
1 True - - High
2 False True - High
3 False False >45K High
4 False False [20K...45K] Moderate
5 False False <20K Low

## But wait... There's more!

```Information doesn't do anything.

We do things with information.```

## Specifying Knowledge

1. What are you talking about?

2. What do you know about it?

``````vocabulary V {
type Price constructed from
{ lessthan20k
, between20And45k
, over45k}

type TheftRating constructed from
{ high
, moderate
, low}

Convertible
CarPrice : Price
HighTheftProbabibilityAuto
PotentialTheftRating : TheftRating
}``````

1. What are you talking about?

2. What do you know about it?

U At Risk Model Convertible Price Potential Theft Rating
Boolean Boolean Currency (\$) {High, Moderate,Low}
1 True - - High
2 False True - High
3 False False >45K High
4 False False [20K...45K] Moderate
5 False False <20K Low
``````PotentialTheftRating = high <- HighTheftProbabibilityAuto.
PotentialTheftRating = high <- Convertible.
PotentialTheftRating = high <- CarPrice = over45k.
PotentialTheftRating = moderate <- CarPrice = between20And45k
& PotentialTheftRating ~= high.
PotentialTheftRating = low <- CarPrice = lessthan20k
& PotentialTheftRating ~= high
& PotentialTheftRating ~= moderate.
``````

## Specifying Knowledge

1. What are you talking about?

## Be useful!

3. What is the required reasoning task?

2. What do you know about it?

## Be useful!

3. What is the required reasoning task?

- Determining Consequences

- Expand to full solution

- Optimisation

Deriving Conclusion From Premises

- Verification

What If?

Handling Incomplete Data

# Potential Theft Rating

## demo

• Demonstrate the different reasoning tasks

The self-driving cars arrive!

# Self-driving cars

## demo

Modify the knowledge with an additional property

### DMChallenge: Make a good burger

• <3000mg Sodium
• <150g Fat
• <3000 Calories
• Servings Ketchup = Servings Lettuce
• Servings Pickles = Servings Tomatoes
• At most 5 of each item

# Make a good burger

## demo

• Optimisation: Maximum Priced Burger
• Optimisation: Minimum Priced Burger

### Profit

Constraint Programming (CP)

Satisfiability Modulo Theories (SMT)

Theorem Provers

...

# Combining DMN and the Knowledge Base Paradigm for Flexible Decision Enactment

Ingmar Dasseville, Laurent Janssens,

Gerda Janssens, Jan Vanthienen,Marc Denecker

http://krr.bitbucket.org/autoconfig

#### RuleML: Autoconfig

By Ingmar Dasseville

• 1,595