The document describes a proposed conversational agent (CA) architecture for serious games that aims to favor knowledge discovery. It consists of two levels: a strategy level manager that controls the dialogue and a tactics level manager that responds to player questions by analyzing them and selecting the best answer from its knowledge base. The CA would take on roles like instructors and provide context-specific knowledge to players to help them learn. An example use case of promoting cultural heritage in a serious game is described. Future work involves further testing the system with users and improving the question handling capabilities.

1. ELIOS Lab
Towards a Conversational Agent
Architecture to Favor Knowledge
Discovery in Serious Games
F. Bellotti, R. Berta, A. De Gloria, E. Lavagnino
ELIOS Lab, DIBE, University of Genoa
berta@elios.unige.it
@riccardoberta
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2. Outlook
Definition of Conversational Agent (CA) in Serious
Games
Requirements
Presentation of the proposed system
An example of use
Future research directions
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3. Conversational Agents (CA)
Non-Player Characters (NPC)
controlled by the computer
able to dialogue with users in natural language
to increase the situation realism and user involvement
usually employed in
 virtual world applications (for training, gaming or advertising)
CAs take part in the narrative, playing a specific role:
to introduce “back-stories”
to assign tasks to players
to reward performance
in general, to give information
Our system is focused on instructional dialogues in
Serious Games
favoring a player’s acquisition of knowledge on specific topics
easy and efficient writing and maintenance of the CA knowledge
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4. Requirements
CA embodies certain well-defined units of knowledge
for instance, a real policeman is able to give street directions,
while a art expert may answer questions about artworks and
heritage
CAs are aimed at answering player’s questions
to help in specific knowledge acquisition
The context can be usefully exploited to facilitate the
dialogue
sample elements of the context are the appearance of the CA
(e.g., uniform, cloths, sex, age), its position, role and game level
in which it appears
CA’s knowledge should be easy to edit and maintain
also by people with no specific expertise in automatic dialogue
algorithms
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5. Requirements
These requirements are not general, but address a
specific target of human-content interactions
this kind of CAs may be employed in short dialogues where a
player could get knowledge clues about a specific topic
this is a typical need of serious games (like adventures)
The main idea is that such CAs implement a specific
serious game mechanism combining:
user knowledge acquisition
natural and pleasant interaction
The key element in our view is player knowledge
acquisition
not only entertain the player with “real” dialogues
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6. The system
We have developed a CA system
to provide useful information on the basis of the user request
to ask player questions in order to stimulate reasoning (e.g., about
facts and context)
The system is based on a two level architecture:
a strategy level (The Strategy Manager)
 it is responsible for managing the high-level aspects of the conversation
a tactical level (The Tactic Manager)
 it responds to the player’s queries by relying on a combination of simple
syntactical analysis and an statistical procedurea
We are testing the system in two real SGs:
SeaGame
 for promoting safe maritime behavior in coastal areas
 a national project funded by Liguria Region (Italy)
Travel in Europe
 for cultural heritage promotion
 A EU project funded by Culture 2000 framework
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7. The Strategy Manager (SM)
Manage the beginning and the end of the
conversation.
For instance, SM could welcome the player and invite him to
speak, introduce itself, answer to a welcome, etc.
An important first step is the presentation of an overview of the
knowledge available by the CA
Change the knowledge set of the tactical level
Exploiting information coming from the user answers and queries
Intervene during the conversation
providing help/indications if the conversation is lagging
Terminate the conversation
when the CA’s knowledge has been told to the player
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8. The Tactics Manager (TM)
Responsible for directly reacting to the user input
Not the whole corpus of possible answer documents is analyzed
The corpus is structured in answers to different types of
questions
 Where, When, Who, Why, What, How
 The five Ws in journalism are the basis for information-gathering
At runtime:
when the user inputs a text, the actual question statement is
extracted
if a question cannot be detected, the TM invites the player to ask
questions (giving clues about the topic)
otherwise, the question is processed in order to catalog it in one
of the W types
then the best answer of that type is selected
if no answer shows a sufficient level of precision, the SM
intervene for managing the situation
 e.g. by outputs an author-defined expression for that situation
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9. The Authoring Tool
Need to allow authors to write dialogue content in a
simple, efficient and effective way
In particular, the author has to prepare:
The set of welcome questions for the SM
The help texts and the trigger conditions through which the
SM proactively intervenes to support the player
The texts of all the possible answers for the TM
 represent the specific knowledge of the CA.
 every answer has to be tagged with the corresponding W type
The author can also set dependencies among questions
one question may enable the player to explore further knowledge
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10. Use Case (cultural heritage)
Travel in Europe game aims at promoting the cultural
heritage of European cities of art
the user explores reconstructed areas of EU cities, while
accomplishing missions in a treasure hunts with artistic topics
during the exploration the player may encounter some CAs with
which he can interact in order to get information about the visited
areas
We present only one situation (other examples can be
found in the paper):
The player has to visit “Palazzo Bianco” (an artistic Renaissance
Palace holding a Museum in the Genoa city center) and collect
information about the palace and the museum
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11. Use Case (cultural heritage)
Player: Is there a museum inside?
CA: Yes, there is a museum inside
Player: At what time does it open?
CA: The museum opens at 3 pm.
Player: Why is it named Palazzo Rosso?
CA: It is named Palazzo Rosso because of its red color
Player: When was it built?
CA: Palazzo Rosso was built in the 17th Century
Player: Who designed Palazzo Rosso?
CA: Architect Pietro Antonio Corradi designed Palazzo
Rosso
Player: What can I see inside?
CA: You can see a lot of pictures inside
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12. Use Case (cultural heritage)
Player: Who are the painters of the pictures exhibited?
CA: The painters are Rembrandt and Van Dick
Player: Which are the most important pictures of
Rembrandt?
CA: The pictures by Rembrandt are the following […]
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13. Conclusion and Future Work
SGs represent a promising tool for improving instruction
Conversation can provide an important added value
We propose a system with a focus on instructional
dialogues
to favor player acquisition of knowledge on specific topics
we have designed the system and prototyped it in two real
examples of SG
Next steps:
extensive testing phase with real user
system performance improvement in particular in the question
handling
 type recognition
Implementation of API to include CAs in standard game engine
(Unity)
Implementation of a GUI for authors
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14. Thank you!
www.galanoe.eu
GALA EU NoE on
Serious Games (#galanoe )
@riccardoberta
www.elios.dibe.unige.it
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