At the Computers as Social Agents workshop @ IVA2013, Edinburgh, August 2013 Abstract: Artificial agents of many kinds increasingly intrude into the human sphere. SatNavs, help systems, automatic telephone answering systems, and even robotic vacuum cleaners are positioned to do more than exist on the side-lines as potential tools. These devices, intentionally or not, often act in a way that in- trudes into our social life. Virtual assistants pop up offering help when an error is encountered, the robot vacuum cleaner starts to clean while one is having tea with the vicar, and automated call handling systems refuse to let you do what you want until you have answered a list of questions. This paper addresses the problem of how to produce artificial agents that are less socially inept. A distinction is drawn between things which are operationally available to us as human conversational- ists and the things that are available to a third party (e.g. a scientists or engineer) in terms of an explicit explanation or representation. The former implies a de- tailed skill at recognising and negotiating the subtle and context-dependent rules of human social interaction, but this skill is largely unconscious – we do not know how we do it, in the sense of the later kind of understanding. The paper proposes a process that bootstraps an incomplete formal functional understanding of hu- man social interaction via an iterative approach using interaction with a native. Each cycle of this iteration entering and correcting a narrative summary of what is happening in recordings of interactions with the automatic agent. This interac- tion is managed and guided through an “annotators’ work bench” that uses the current functional understanding to highlight when user input is not consistent with the current understanding, suggesting alternatives and accepting new sug- gestions via a structured dialogue. This relies on the fact that people are much better at noticing when dialogue is ”wrong” and in making alternate suggestions than theorising about social language use. This, we argue, would allow the itera- tive process to build up understanding and hence CA scripts that fit better within the human social world. Some preliminary work in this direction is described.

1. Capturing the Implicit - an iterative approach to enculturing artificial agents, Peter Wallis & Bruce Edmonds, CASA@IVA, Edinburgh, August 2013. slide 1
Capturing the Implicit
an iterative approach to enculturing artificial agents
Peter Wallis and Bruce Edmonds
Centre for Policy Modelling
Manchester Metropolitan University

2. Capturing the Implicit - an iterative approach to enculturing artificial agents, Peter Wallis & Bruce Edmonds, CASA@IVA, Edinburgh, August 2013. slide 2
The Issue: Producing Social Actors
• Computational devices and software increasingly
intrude into the human social sphere (e.g.
conversational help systems, satnavs, robot
vacuum cleaners, telephone answering systems)
• Most (if not all) of these are, at best, socially inept
and, at worst, downright rude
• To produce socially competent humans takes
(roughly) 20 years of effort by society
• This paper looks at how one might produce a
socially competent artificial agent

3. Capturing the Implicit - an iterative approach to enculturing artificial agents, Peter Wallis & Bruce Edmonds, CASA@IVA, Edinburgh, August 2013. slide 3
The Centrality of Social Intelligence
• We are a fundamentally social species
• We achieve most of society’s products collectively
• The intelligence that organises these
achievements are both cognitive (between brain
cells in our brain) and social (between individuals)
• Indeed the “Social Intelligence Hypothesis”
suggests the crucial evolutionary advantage our
brains give us is in terms of social abilities
• Our “individual intelligence” is a side effect of our
“social intelligence” (not the other way around)

4. Capturing the Implicit - an iterative approach to enculturing artificial agents, Peter Wallis & Bruce Edmonds, CASA@IVA, Edinburgh, August 2013. slide 4
The prevailing social “autism” of
artificial devices
• Given the centrality of social intelligence and
interaction to human life…
• …it is very odd that our artifacts are designed with
individual intelligence as central and any social
attributes as an afterthought (if they have any)
Some reasons for this might be:
• The prevailing understanding of computers etc. as
tools to be engineered for a specific purpose
• The gulf between AI/cognitive science and the
social sciences
• The sheer difficulty of engineering social abilities

5. Capturing the Implicit - an iterative approach to enculturing artificial agents, Peter Wallis & Bruce Edmonds, CASA@IVA, Edinburgh, August 2013. slide 5
The Importance of “Folk Theory” in
communication and co-adaption
• The classic model of language involves a set of
signs with fixed meaning
• Instead, signs are largely interpreted in context
and part of the continuum from action to
communication
• We humans model each other in order to figure
out what a conversational partner really means
• Using a common understanding of how other
people’s minds work: intention etc. (“folk theory”)
• … and we work hard at it

6. Capturing the Implicit - an iterative approach to enculturing artificial agents, Peter Wallis & Bruce Edmonds, CASA@IVA, Edinburgh, August 2013. slide 6
Eggins & Slade (1997) on sequential
relevance
A: What's that floating in the wine?
B: There aren't any other solutions.
"You will try very hard to find a way of interpreting B's turn as somehow
an answer to A's question, even though there is no obvious link between
them, apart from their appearance in sequence. Perhaps you will have
decided that B took a common solution to a resistant wine cork and
poked it through into the bottle, and it was floating in the wine. Whatever
explanation you came up with, it is unlikely that you looked at the
example and simply said `it doesn't make sense', so strong is the
implication that adjacent turns relate to each other.”

7. Capturing the Implicit - an iterative approach to enculturing artificial agents, Peter Wallis & Bruce Edmonds, CASA@IVA, Edinburgh, August 2013. slide 7
The Difficulty of Accessing Social
Knowledge
• Most social knowledge (how to productively interact
with other humans) is implicit – that is, the people
wielding that knowledge do so without “thinking about
it” (in the sense of conscious explicit thought)
• Thus people have difficulty explaining how they do
social things (indeed they are often unaware of these
skills but simply adapt/use them)
• Social knowledge is extensive, messy and highly
context-dependent
• High levels of social skills, with respect to any
particular social, are only acquired by humans by
living within that culture for many years: adapting,
making mistakes, observing others, interacting…

8. Capturing the Implicit - an iterative approach to enculturing artificial agents, Peter Wallis & Bruce Edmonds, CASA@IVA, Edinburgh, August 2013. slide 8
Kinds of Utterance
Paul Seedhouse (2004) summarizing the outcomes
of Conversation Analysis (with added numbering):
“a conversational partner’s utterance will go
1. seen but unnoticed
2. noticed and accounted for, or
3. risk sanction”
• Things that are accountable for and polite, have a
role and do not necessarily consciously stand out
• Anything that is not easily accountable for sticks
out and would normally trigger further dialogue to
understand them

9. Capturing the Implicit - an iterative approach to enculturing artificial agents, Peter Wallis & Bruce Edmonds, CASA@IVA, Edinburgh, August 2013. slide 9
Aim of our approach
• To implement a feasible approach – not aiming for
perfect social abilities, but at least to make
artificial entities less socially inept
• To start with an existing entity that exists within
the social sphere, and make some improvements
• To produce an approach that mimics (in a small
way) some of the enculturation that humans
undergo, but leveraged by technology to elicit the
most social knowledge possible (which will be
limited)
• As a consequence understand more about
making and deploying social devices

10. Capturing the Implicit - an iterative approach to enculturing artificial agents, Peter Wallis & Bruce Edmonds, CASA@IVA, Edinburgh, August 2013. slide 10
Principles of the Approach
• A iterative approach – stepwise improvement of social
knowledge and abilities (as opposed to a one-off
analyse-specify-design method)
• Each interation based on records/logs of device in
situ, interacting with humans in current state
• Relying on the shared “folk theory” of cognition
concerning what normally people do and why
• Also that what goes wrong in interactions is much
easier to spot that what goes right
• Facilitating a human (in a natural, non-CA-expert
mode) to annotate logs with social knowledge
• Implementing a repair strategy to fix these faults

11. Capturing the Implicit - an iterative approach to enculturing artificial agents, Peter Wallis & Bruce Edmonds, CASA@IVA, Edinburgh, August 2013. slide 11
Diagram of a Standard NL Approach

12. Capturing the Implicit - an iterative approach to enculturing artificial agents, Peter Wallis & Bruce Edmonds, CASA@IVA, Edinburgh, August 2013. slide 12
An Ethnomethods Approach

13. Capturing the Implicit - an iterative approach to enculturing artificial agents, Peter Wallis & Bruce Edmonds, CASA@IVA, Edinburgh, August 2013. slide 13
Leveraging Common Knowledge of
Folk Theory

14. Capturing the Implicit - an iterative approach to enculturing artificial agents, Peter Wallis & Bruce Edmonds, CASA@IVA, Edinburgh, August 2013. slide 14
A Summary of Our Approach

15. Capturing the Implicit - an iterative approach to enculturing artificial agents, Peter Wallis & Bruce Edmonds, CASA@IVA, Edinburgh, August 2013. slide 15
Towards facilitating social annotation
using a “social” workbench
The workbench
prompts the “folk
theorist” user to notice
bits that seem wrong
with the conversation
and then using a
series of pre-scripted
prompts tries to
capture what the user
thinks is wrong in a
slightly constrained
(i.e. somewhat
standardised) way.

16. Capturing the Implicit - an iterative approach to enculturing artificial agents, Peter Wallis & Bruce Edmonds, CASA@IVA, Edinburgh, August 2013. slide 16
Machine conversation (circa „97)
Machine: G'day mate.
Human: G'day. How's it going?
Machine: My mother once said "How is it going is a mystery." She was a
bit nuts.
Human: Uh-huh.
Machine: Are you still learning to speak, my friend?
Human: NO, no. But your anecdote took me a bit by surprise.
How was your morning?
Machine: Oh well, that's a pity!
Human: I'm sorry; what's a pity?
Machine: Apologies accepted.
Human: I feel compelled to insult you, and we've only been talking a
minute.
Machine: We are bloody well talking, aren't we?
Human: Oh baby. We sure are. So here are some keywords for you:
shit mother baseball.
...

17. Capturing the Implicit - an iterative approach to enculturing artificial agents, Peter Wallis & Bruce Edmonds, CASA@IVA, Edinburgh, August 2013. slide 17
(Lots of) Further Issues
• Work still very much in infancy
• At the moment it takes an expert engineer to
interpret the knowledge elicited by the system in
terms of repair to the original conversational
scripts
• The best form for the database of social
knowledge about the interactions needs to be
established
• How such an iterative elicitation approach can
work alongside traditional scripting NL modelling
approaches and models is unclear

18. Capturing the Implicit - an iterative approach to enculturing artificial agents, Peter Wallis & Bruce Edmonds, CASA@IVA, Edinburgh, August 2013. slide 18
The Future?

19. Capturing the Implicit - an iterative approach to enculturing artificial agents, Peter Wallis & Bruce Edmonds, CASA@IVA, Edinburgh, August 2013. slide 19
Thanks!
Peter Wallis
http://cfpm.org/~peter
Bruce Edmonds
http://bruce.edmonds.name
Centre for Policy Modelling
http://cfpm.org