A heuristic strategy for persuasion dialogues Josh Murphy, Elizabeth - - PowerPoint PPT Presentation

A heuristic strategy for persuasion dialogues

Josh Murphy, Elizabeth Black, Michael Luck

King’s College London josh.murphy@kcl.ac.uk

May 19, 2016

Josh Murphy, Elizabeth Black, Michael Luck (KCL) A heuristic strategy for persuasion dialogues May 19, 2016 1 / 18

Overview

1

Persuasion dialogues

2

Current methods for computing a dialogue strategy

3

Heuristic strategy

4

Results

5

Future work

Josh Murphy, Elizabeth Black, Michael Luck (KCL) A heuristic strategy for persuasion dialogues May 19, 2016 2 / 18

Argument-based persuasion dialogues

Initial condition: Agents have conflicting views on a topic. Individual goals: At least one agent aims to persuade the other that the topic is acceptable/unacceptable. Goals of the dialogue: Attempt to resolve a conflicting view on the topic.

Josh Murphy, Elizabeth Black, Michael Luck (KCL) A heuristic strategy for persuasion dialogues May 19, 2016 3 / 18

Example dialogue

Persuader A B T C D Persuadee T E C F Global Knowledge A B T C D F E

Josh Murphy, Elizabeth Black, Michael Luck (KCL) A heuristic strategy for persuasion dialogues May 19, 2016 4 / 18

Example dialogue

Persuader A B T C D Persuadee T E C F A A

Josh Murphy, Elizabeth Black, Michael Luck (KCL) A heuristic strategy for persuasion dialogues May 19, 2016 5 / 18

Example dialogue

Persuader A B T C D Persuadee T E C F A D D

Josh Murphy, Elizabeth Black, Michael Luck (KCL) A heuristic strategy for persuasion dialogues May 19, 2016 6 / 18

Current methods for computing a dialogue strategy

AI planning [Black et al., 2014]. Mixed observability Markov decision problems [Hadoux et al., 2015] Minimax algorithm [Rienstra et al., 2013] However, none of these approaches have been shown to scale to domains with more than 10 arguments.

Josh Murphy, Elizabeth Black, Michael Luck (KCL) A heuristic strategy for persuasion dialogues May 19, 2016 7 / 18

Heuristic strategy

We want a strategy that can be computed in domains with many arguments, even if the strategy is not optimal. To find a time-efficient strategy we consider the local topological properties of arguments graphs to determine some estimate of how beneficial an argument would be if asserted.

Josh Murphy, Elizabeth Black, Michael Luck (KCL) A heuristic strategy for persuasion dialogues May 19, 2016 8 / 18

Estimating the influence of arguments

To obtain an accurate estimate of how beneficial asserting an argument will be we want to take into account: Does the argument support or defend the topic? What is the estimated of influence the argument has over the topic?

Josh Murphy, Elizabeth Black, Michael Luck (KCL) A heuristic strategy for persuasion dialogues May 19, 2016 9 / 18

Estimating the influence of arguments

Does the argument support or defend the topic? A B C T D F E

Josh Murphy, Elizabeth Black, Michael Luck (KCL) A heuristic strategy for persuasion dialogues May 19, 2016 10 / 18

Estimating the influence of arguments

What is the estimated influence an argument has over the topic? A B C T D E J K H I F G

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Estimating the influence of arguments

A B C T D E F G T D G F E C C C +1

• 1/2
• 1/8

+1/4 +1/16

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Estimating the influence of arguments

Persuader A B T C D Persuadee T E C F Global Knowledge A B T C D F E

Josh Murphy, Elizabeth Black, Michael Luck (KCL) A heuristic strategy for persuasion dialogues May 19, 2016 13 / 18

Experimental evaluation

Evaluation through simulation. Generate thousands of instances of dialogue scenarios.

I Requires many argumentation frameworks to act as a domain I Ideally, from real-world sources — but, public databases not large

enough for serious empirical evaluation.

I So, generate random frameworks with “realistic” properties

Measure if the persuader is successful when using the heuristic strategy, and how long computing the strategy takes. Use a random strategy as a lower bound on success.

Josh Murphy, Elizabeth Black, Michael Luck (KCL) A heuristic strategy for persuasion dialogues May 19, 2016 14 / 18

Results

The heuristic strategy is fast to compute, and efficiently scales to domains with 50 arguments.

Args 10 20 30 40 50 Time <0.1 0.21 0.37 0.56 0.77 Table: Time to compute heuristic strategy (seconds). Args is the number of arguments in the domain.

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Results

The heuristic strategy has a high success rate.

Figure: Percentage success rate of strategies. Error bars indicate standard error.

Josh Murphy, Elizabeth Black, Michael Luck (KCL) A heuristic strategy for persuasion dialogues May 19, 2016 16 / 18

Future work

Application of the strategy to more complex dialogue scenarios.

I Particularly dialogues with more than two participants.

The generation of argumentation frameworks for the use in simulation has a large effect on the resulting dialogue.

I What structures of framework exist in real-world domains? Different

argumentation schemes, argument mining of different sources, models

• f argument (extended frameworks)...

I What impact do different structures have on other argument-based

systems? Dialogues, argument solvers, dynamic argumentation...

Josh Murphy, Elizabeth Black, Michael Luck (KCL) A heuristic strategy for persuasion dialogues May 19, 2016 17 / 18

References

• E. Black, A. Coles, S. Bernardini (2014)

Automated planning of simple persuasion dialogues Computational Logic in Multi-Agent Systems, LNCS vol. 8624, Springer, 87 – 104.

• E. Hadoux, A. Beynier, N. Maudet, P. Weng, A. Hunter (2015)

Optimization of probabilistic argumentation with Markov decision models. In Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence, AAAI Press, 2004 – 2010.

• T. Rienstra, M. Thimm, N. Oren (2013)

Opponent models with uncertainty for strategic argumentation. In Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence, AAAI Press, 332 – 338.

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