DiArg - An Argumentation-based Dialogue Reasoner

DiArg is a Java library for argumentation-based dialogue reasoning (introduced in this paper). The focus of DiArg is the management of argumentation framework sequences. During a dialogue, arguments and attacks are iteratively added to an argumentation framework and the framework is resolved after each iteration (i.e., after a set of arguments and attack relations have been added). For such types of dialogues, DiArg provides object-oriented abstractions (the initial argumentation framework and its additions are managed in an argumentation sequence object), as well as the following features:

1. Expansion management. DiArg can enforce (if desirable) that each argumentation framework in an argumentation sequence is, with respect to its predecessor, an expansion (only additions to the preceding framework), or a normal expansion (only additions of arguments and no additions of attack relations between existing arguments).

2. Enforcement of consistent (reference independent) conclusions. DiArg can enforce the reference independence of the conclusions that are made at the different iteration steps; i.e., when normally expanding an argumentation framework, an argument that has previously been considered a valid conclusion can only be discarded if any of the new arguments is now considered a valid conclusion. Similarly, DiArg can enforce cautious monotony across iterations.

3. Context management. DiArg provides a context abstraction that allows for the deactivation of specific argument sets at any iteration step. For example, given the application scenario of a health recommender system, certain arguments might be deactivated during weekends, to allow the user to deviate from the otherwise strict dietary recommendations.

You find the Javadoc of this project at https://people.cs.umu.se/~tkampik/docs/diarg/.

Note that DiArg supports the Shkop argumentation approach. You find a tutorial here.

Installation

The installation instructions assume Gradle is used for the build setup. The library is available in a custom Maven repository, which needs to be added to the project's configuration.

repositories {
    maven {
        url 'https://people.cs.umu.se/~tkampik/mvn/'
    }
    ...
}

Then, the dependency to DiArg can be added as follows:

dependencies {
    compile group: 'diarg', name: 'di-arg', version: '0.4-SNAPSHOT'
    ...
}

DiArg needs to be used in combination with Tweety, which can be added as follows:

dependencies {
    ...
    implementation('net.sf.tweety:tweety-full:1.16') {
        exclude group: 'org.ojalgo', module: 'ojalgo'
        exclude group: 'jspf', module: 'core'
    }
    ...

Note that this excludes some Tweety dependencies that are not required for most Tweety/DiArg applications.

Finally, import DiArg as follows:

import diarg.*;
import diarg.enums.*;

Tutorial

To show how DiArg can be applied, let us introduce the following application example. We are developing a digital assistant (mobile application) for stress management. The application recommends stress-relieving activities (represented by arguments) to an end-user; the end-user can then either accept the recommendation and add it to their schedule or reject the activity by adding an argument that attacks the activity. This argument will be considered when providing future recommendations. The argument may be context-dependent. For example, a user may reject stress-relieving activities that are particularly time-consuming during weekdays, but accept them at weekends.

Let us present a simplified example program that models such a recommender system. We start with an argumentation framework that has three arguments representing the execution of different activities. Because at any point in time, only one activity can be executed, all arguments attack each other. Note that for defining argumentation frameworks, we use the Tweety library.

DungTheory initialFramework = new DungTheory();
Argument a = new Argument("a"); // Meditate
Argument b = new Argument("b"); // Join social lunch
Argument c = new Argument("c"); // Go hiking
initialFramework.add(a);
initialFramework.add(b);
initialFramework.add(c);
initialFramework.addAttack(a, b);
initialFramework.addAttack(b, a);
initialFramework.addAttack(b, c);
initialFramework.addAttack(c, b);
initialFramework.addAttack(c, a);
initialFramework.addAttack(a, c);

In the program code above, we define the argumentation framework AF = ({a, b, c}, {(a, b), (b, a), (b, c), (c, b), (c, a), (a, c)}). It is clear that a relatively credulous argumentation semantics should be used for the application scenario. In this example, we use CF2 semantics:

Semantics cf2Semantics = new Semantics(SemanticsType.CF2);

Then, the recommender system can either allow the user to choose any of the extensions a specific semantics returns for the framework, or it can add an annihilator argument to the framework to enforce a unique extension.

Let us assume we want the system to do the latter. For this, we add the following program code:

AFSequence sequence = new AFSequence(
        SequenceType.NORMALLY_EXPANDING,
        ResolutionType.EXPANSIONIST_REFERENCE_INDEPENDENT,
        cf2Semantics,
        true);
sequence.addFramework(initialFramework);

Note that we have configured the sequence to have the following properties:

• In the sequence, each argumentation framework must be a normal expansion of its predecessor.
• The conclusion of an argumentation framework is determined by expanding the framework until a unique (exactly one) extension is determined that implies consistent inferences considering previous conclusions; i.e., rejecting an argument of a previous conclusion requires the inclusion of a new argument in the conclusion, or a change in contexts.
• Extensions of argumentation frameworks are determined using rational conflict-free (RCF) semantics.
• We allow for the definition of contexts.

Now, we resolve the initial framework:

Extension resolution0 = sequence.resolveFramework(0);
System.out.println(String.format("Initial recommendation: %s", resolution0));

The system adds an annhilator argument to the initial framework to generate a normal expansion that has exactly one extension, given the specified semantics. In our case, the system adds the argument an_a (the name may differ) to the framework, and the attack (an_b, b). It follows that the conclusion is {an_b, c}, i.e., the conclusion is {c} when excluding the annihilator.

>>> Initial recommendation: {c}

In our application scenario, the conclusion {c} implies that our recommender system suggests Go hiking as the stress-relieving activity to the end-user. Now, let us assume that the user wants to reject the recommendation because she does not have time to go hiking on weekdays. For this, she inserts this feedback through the system's user interface, which generates the next framework in our sequence:

DungTheory initialUserFeedback = new DungTheory();
initialUserFeedback.add(initialFramework);
Argument d = new Argument("d");
initialUserFeedback.add(d);
initialUserFeedback.addAttack(d, c);
sequence.addFramework(initialUserFeedback);

The sequence is then resolved as follows:

Extension resolution1 = sequence.resolveFramework(1);
System.out.println(String.format("Recommendation after user feedback: %s", resolution1));
>>> Recommendation after user feedback: {b,d}

Note that because of the remaining attack cycle "a attacks b attacks a", the system again uses an annihilator argument to generate a single recommendation (exactly one extension).

We assume that to manage the temporal dimension of user feedback ("on weekdays"), the system supports temporal contexts. For the sake of simplicity, let us say the temporal context can be weekdays or weekend (or none). Hence, upon receiving the user feedback, the system also creates the weekend context and adds argument d to it, which implies that argument d and its attacks are not considered when resolving a framework in the sequence that has the weekend context assigned to it:

Extension contextArguments = new Extension();
contextArguments.add(d);
Context context = new Context("weekend", contextArguments);
Collection<Context> contexts = new LinkedList();
contexts.add(context);

The system can then generate a recommendation with an active weekend context as follows:

sequence.addFramework(initialUserFeedback, contexts);
Extension resolution2 = sequence.resolveFramework(2);

The framework is resolved in a reference independent manner w.r.t. to its closest predecessor with whose contexts it is aligned; i.e., in our case, the system again generates the initial recommendation:

System.out.println(String.format("Recommendation after context switch: %s", resolution2));
>>> Recommendation after context switch: {c}

Note that the enforcement of reference independence implies that given an argumentation framework AF' and its predecessor AF, with AF' being a normal expansion of AF, if the conclusion derived AF' is different from the conclusion derived from AF, the conclusion of AF' must contain an argument that is not in AF.

The full source code of the example is available here.

Rational Conflict-Free Semantics

DiArg provides a reasoner for Rational Conflict-Free (RCF) RCF semantics are a non-SCC-recursive, CF2-like semantics (with the difference that RCF is resilient to (not affected by) self-attacking arguments). Given an argumentation framework AF = (AR, AT) a set S subset of (or equal to) AR is a RCT extension iff:

1. S is a maximal conflict-free subset of AF and all arguments that are only attacked by self-attacking arguments are in S.
2. The reachable range of S is maximal (w.r.t. set inclusion) among all sets that fulfill 1.
3. The reachably defended range of S is maximal (w.r.t. set inclusion) among all sets that fulfill 1.

, where:

• the reachable range of a set S is the union of S with all arguments that are reachable from S;
• the reachably defended range of a set S is the union of S with all arguments that are attacked by an argument a of S, such that a is defended by arguments in S that are not reachable from a against all attackers that are reachable from a.

The reasoner can be used as follows:

import diarg.SimpleNSACF2Reasoner;

import net.sf.tweety.arg.dung.syntax.Argument;
import net.sf.tweety.arg.dung.syntax.Attack;
import net.sf.tweety.arg.dung.syntax.DungTheory;
import net.sf.tweety.arg.dung.semantics.Extension;

import java.util.Collection;

public class RCFExample {

    public static void main(String[] args){
        SimpleRCFReasoner rcfReasoner = new SimpleRCFReasoner();

        DungTheory framework = new DungTheory();
        Argument a = new Argument("a");
        Argument b = new Argument("b");
        Argument c = new Argument("c");
        Argument d = new Argument("d");

        framework.add(a);
        framework.add(b);
        framework.add(c);
        framework.add(d);
        framework.add(new Attack(a, b));
        framework.add(new Attack(b, a));
        framework.add(new Attack(a, c));
        framework.add(new Attack(b, c));
        framework.add(new Attack(c, d));

        Collection<Extension> extensions = rcfReasoner.getModels(framework);
        System.out.println(extensions);

    }
}

A set of examples is available as test cases here.

Development and Testing

Checkout the source code:

git clone [email protected]:TimKam/diarg.git.

Build the project:

./gradlew build

Run unit tests:

./gradlew test

License

BSD 2-Clause License

Authors

• Timotheus Kampik
• Dov Gabbay

Cite as:

@inproceedings{kampik2020towards,
  title={Towards DiArg: An Argumentation-based Dialogue Reasoning Engine},
  author={Kampik, Timotheus and Gabbay, Dov},
  booktitle={safa2020: The Third International Workshop on Systems and Algorithms for Formal Argumentation, Online, September 8, 2020},
  year={2020}
}

Implements theoretical work by Timotheus Kampik, Dov Gabbay, and Juan Carlos Nieves.