The document proposes a model for generating explanations from argumentation-based agents that use an extended Belief-based Goal Processing (BBGP) model for intention formation. It formalizes the BBGP model and intention formation process using argumentation frameworks. Explanations are generated by constructing sub-argumentation frameworks related to goals from the overall argumentation framework representing the agent's reasoning. This allows generating both partial and complete explanations by analyzing acceptable arguments under different semantics. The approach is illustrated on a rescue robot scenario.

1. Introduction Background Proposal Conclusions and Future Work
Argumentation-based Agents that
Explain their Decisions
Mariela Morveli Espinoza1, Ayslan Possebom2, and Cesar A. Tacla1
1 Program in Electrical and Computer Engineering, Federal University of Technology of Paraná, Curitiba
2 Federal Institute of Parana, Paranavai
October 17 2019
Mariela Morveli Espinoza et al. CPGEI-UTFPR Argumentation-based Agents that Explain their Decisions October 17 2019 1 / 18

2. Introduction Background Proposal Conclusions and Future Work
Outline
1 Introduction
Motivation
Problem
Proposal Overview
2 Background
Argumentation
3 Proposal
Formalization of the BBGP model
Generating the explanations
4 Conclusions and Future Work
Mariela Morveli Espinoza et al. CPGEI-UTFPR Argumentation-based Agents that Explain their Decisions October 17 2019 2 / 18

3. Introduction Background Proposal Conclusions and Future Work
Motivation
Motivating Example : Rescue robots
Some goals of a rescue robot in a scenario of a natural
disaster
Wander the area searching people needing help,
Take severely injured people to the hospital,
Send healthy people to the shelter
...
When the robot finds a person, it has to decide what goal to pursue based on its
perceptions (beliefs).
After the rescue work, the robots can be asked for an explanation of why a
wounded person was sent to the shelter instead of taking him/her to the hospital,
or why the robot decided to take to the hospital a person x first, instead of taking
another person y.
Therefore...
It is important to endow the agents (maybe robots) with the ability of
explaining their decisions about the goals they pursued or are pursuing.
Mariela Morveli Espinoza et al. CPGEI-UTFPR Argumentation-based Agents that Explain their Decisions October 17 2019 3 / 18

4. Introduction Background Proposal Conclusions and Future Work
Problem
Problem
BDI Agents
Beliefs about itself, other agents, and its environment
Desires about future states
Intentions about its own future actions
Limitations
BDI agents there is only two stages in the intention formation process. This means
that there is a lack of a fine-grained analysis of this process, which may
improve and enrich the informational quality of the explanations.
BDI agents are not endowed with explainability abilities.
Research Questions
1 How to improve the analysis of the intention formation process?
2 How can explanations be generated by BDI (or extended) agents?
Mariela Morveli Espinoza et al. CPGEI-UTFPR Argumentation-based Agents that Explain their Decisions October 17 2019 4 / 18

5. Introduction Background Proposal Conclusions and Future Work
Proposal Overview
Proposal Overview
1 An extended model for intention formation has been proposed by Castelfranchi
and Paglieri (2007), which was named the Belief-based Goal Processing model
(let us denote it by BBGP model). The BBGP model has four stages :
activation
evaluation
deliberation
checking
Four different statuses for a goal are defined :
active (=desire)
pursuable
chosen
executive (=intention).
2 Argumentation-based approach. In the intention formation process, arguments
can represent reasons for a goal to change (or not) its status.
Mariela Morveli Espinoza et al. CPGEI-UTFPR Argumentation-based Agents that Explain their Decisions October 17 2019 5 / 18

6. Introduction Background Proposal Conclusions and Future Work
Argumentation
Abstract Argumentation [Dung, 1995]
In abstract argumentation frameworks (AFs) statements (called arguments) are
formulated together with a relation (attack) between them.
The conflicts between the arguments are conflicts by means of argumentation
semantics.
Mariela Morveli Espinoza et al. CPGEI-UTFPR Argumentation-based Agents that Explain their Decisions October 17 2019 6 / 18

7. Introduction Background Proposal Conclusions and Future Work
Argumentation
Abstract Argumentation [Dung, 1995]
In abstract argumentation frameworks (AFs) statements (called arguments) are
formulated together with a relation (attack) between them.
The conflicts between the arguments are conflicts by means of argumentation
semantics.
Mariela Morveli Espinoza et al. CPGEI-UTFPR Argumentation-based Agents that Explain their Decisions October 17 2019 7 / 18

8. Introduction Background Proposal Conclusions and Future Work
Formalization of the BBGP model
BBGP-based Agent
Building Blocks
BBGP-based agents use rule-based systems as their basic reasoning model.
The underlying logical language – denoted by L – consists of a set of literals in
first-order logical language
From L, we distinguish the following finite sets :
F is the set of facts of the agent
G is the set of goals of the agent
We can also distinguish :
A set of strict rules S, which encode strict information that has no exception (→)
A set of defeasible rules D, which expresse general information that may have
exceptions (⇒)
Mariela Morveli Espinoza et al. CPGEI-UTFPR Argumentation-based Agents that Explain their Decisions October 17 2019 8 / 18

9. Introduction Background Proposal Conclusions and Future Work
Formalization of the BBGP model
Formalization of the BBGP Model [Castelfranchi&Paglieri,2007]
ACTIVATION
STAGE
Sleeping
goals
Active
Activation arguments
CHECKING
STAGE
Chosen
Executive goals
or Intentions
Checking arguments
EVALUATION
STAGE
Active goals or
Desires
Pursuable
goals
Evaluation arguments
DELIBERATION
STAGE
goals
Chosen goals
Deliberation arguments
Mariela Morveli Espinoza et al. CPGEI-UTFPR Argumentation-based Agents that Explain their Decisions October 17 2019 9 / 18

10. Introduction Background Proposal Conclusions and Future Work
Formalization of the BBGP model
Rescue robots scenario
Example
Starting mental states of a BBGP-based agent :
F = {b2,b3,b5}
S = {r1
ac ,r2
ac ,r4
}
D = {r3
,r2
}
where :
b2 = has_fract_bone(Tom)
b3 = fract_bone(Tom,arm)
b5 = open_fracture(Tom)
r2
= has_fract_bone(x) ⇒ injured_severe(x)
r3
= fract_bone(x,arm) ⇒ ¬injured_severe(x)
r4
= open_fracture(x) → injured_severe(x)
r1
ac = injured_severe(x) → take_hospital(x)
r2
ac = ¬injured_severe(x) → send_shelter(x)
Some arguments that can be generated :
A = {b5,r4},b7
D = {b3,r3},¬b7
I = {b2,r2},b7
¬ b7
b3
b7
b5
B
g2
A DC
g3
r4
rac
1
r3
rac
2
b7 =injured_severe
g2 =take_hospital
g3 =send_shelter
7
FE
3
2
injured_severe(Tom)
take_hospital(Tom)
send_shelter(Tom)
b7
H
b2
G
g2
I
r2
rac
1
Mariela Morveli Espinoza et al. CPGEI-UTFPR Argumentation-based Agents that Explain their Decisions October 17 2019 10 / 18

11. Introduction Background Proposal Conclusions and Future Work
Formalization of the BBGP model
Rescue robots scenario
¬ b7
b3
b7
b5
B
g2
A DC
g3
r4
rac
1
r3
rac
2
b7 =injured_severe
g2 =take_hospital
g3 =send_shelter
7
FE
3
2
injured_severe(Tom)
take_hospital(Tom)
send_shelter(Tom)
b7
H
b2
G
g2
I
r2
rac
1
E A
C F B D
I HG
FIGURE – Argumentation Framework for the activation
stage. The set of acceptable arguments under
preferred semantics is {A,B,C,E,G,H,I}
.Justified conclusions
This means that the agent believes
has_fract_bone(Tom) (G),
fract_bone(Tom,arm) (E),
open_fracture(Tom) (C), and
injured_severe(Tom) (B,H).
Therefore, the agent activates goal
take_hospital(Tom) (A,I).
Mariela Morveli Espinoza et al. CPGEI-UTFPR Argumentation-based Agents that Explain their Decisions October 17 2019 11 / 18

12. Introduction Background Proposal Conclusions and Future Work
Generating the explanations
Sub Argumentation Frameworks
FIGURE – Step 1
.
FIGURE – Sub-AF 1
FIGURE – Step 2
.
FIGURE – Sub-AF 2
Mariela Morveli Espinoza et al. CPGEI-UTFPR Argumentation-based Agents that Explain their Decisions October 17 2019 12 / 18

13. Introduction Background Proposal Conclusions and Future Work
Generating the explanations
Partial and Complete Explanations
FIGURE – Partial explanations are determined by an ar-
gumentation semantics.
.
FIGURE – Complete explanations are determined by the
whole sub-AF related to a goal.
.
Mariela Morveli Espinoza et al. CPGEI-UTFPR Argumentation-based Agents that Explain their Decisions October 17 2019 13 / 18

14. Introduction Background Proposal Conclusions and Future Work
Generating the explanations
Rescue robots scenario : Why did you take Tom to the hospital instead
of sending him to the shelter?
Sub-AF for goal g2 : take_hospital(Tom)
E A
C F B D
I HG
Arguments for goal g2
E A
C F B D
I HG
Sub-arguments of I
E A
C F B D
I HG
Sub-arguments of A
E A
C F B D
I HG
Attacks to I and H – Final Sub-AF
Mariela Morveli Espinoza et al. CPGEI-UTFPR Argumentation-based Agents that Explain their Decisions October 17 2019 14 / 18

15. Introduction Background Proposal Conclusions and Future Work
Generating the explanations
Rescue robots scenario : Partial and Complete Explanations
C F
I
E
A
B
HG
FIGURE – Partial explanation
.Partial explanation in natural language
Tom had a fractured bone (G), which was
in his arm (E), and it was an open fracture
(C); therefore, he was severely injured
(B,H). Since he was severely injured I took
him to the hospital (A,I).
C F
I
E
A
B
HG
FIGURE – Complete explanation
.Complete explanation in natural language
Tom had a fractured bone (G), which was
in his arm (E). Given that he had a
fractured bone, he might be considered
severe injured (H); however, since such
fracture was of his arm, it might not be
considered a severe injure (F). Finally, I
noted that it was an open fracture (C),
which determines – without exception –
that it was a severe injury (B). For these
reasons I took him to the hospital (A,B).
Mariela Morveli Espinoza et al. CPGEI-UTFPR Argumentation-based Agents that Explain their Decisions October 17 2019 15 / 18

16. Introduction Background Proposal Conclusions and Future Work
Conclusions and Future Work
Conclusions
In order to improve the analysis of the intention formation process, we
chosen the BBGP model, which can be considered an extension of the BDI model.
In order generate explanations, we equipped BBGP-based agents with a
structure and a argumentation-based mechanism to generate both partial and
complete explanations.
Future Work
A goal can also go back in the intention formation process. This was not taken into
account and it is an interesting future research.
We want to deal with more complex questions, which require more elaborate and
adequate explanations. Maybe “good” explanations include elements of more than
one AF.
Mariela Morveli Espinoza et al. CPGEI-UTFPR Argumentation-based Agents that Explain their Decisions October 17 2019 16 / 18

17. Introduction Background Proposal Conclusions and Future Work
References
P. M. Dung, On the acceptability of arguments and its fundamental role in nonmonotonic
reasoning, logic programming and n-person games, Artificial intelligence, vol. 77, no. 2, pp.
321–357, 1995.
C. Castelfranchi and F. Paglieri, The role of beliefs in goal dynamics : Prolegomena to a
constructive theory of intentions, Synthese, vol. 155, no. 2, pp. 237–263, 2007.
M. Morveli-Espinoza, A. T. Possebom, J. Puyol-Gruart, and C. A. Tacla, Argumentation-based
intention formation process, DYNA, vol. 86, no. 208, pp. 82–91, 2019.
S. Anjomshoae, A. Najjar, D. Calvaresi, and K. Framling, Explainable agents and robots :
Results from a systematic literature review, in Proceedings of the 18th International
Conference on Autonomous Agents and MultiAgent Systems, 2019, pp. 1078–1088.
Mariela Morveli Espinoza et al. CPGEI-UTFPR Argumentation-based Agents that Explain their Decisions October 17 2019 17 / 18

18. Introduction Background Proposal Conclusions and Future Work
Thank you!
Questions?
Please, send me an e-mail to morveli.espinoza@gmail.com
Mariela Morveli Espinoza et al. CPGEI-UTFPR Argumentation-based Agents that Explain their Decisions October 17 2019 18 / 18