Towards a Context-Sensitive Structure for Behavioural Rules (Context, Scope, Antecedents, and Results)

Towards a Context-Sensitive
Structure for Behavioural Rules
(Context, Scope, Antecedents, and Results)

Bruce Edmonds
Centre for Policy Modelling,
Manchester Metropolitan University

Towards a Context-Sensitive Structure for Behavioural Rules, Bruce Edmonds, Informal Workshop on Qual. Evidence & Rules, MMU, Sept. 2012, slide 1

Summary of Talk: a view from
Cognitive Science
Suggest dividing behavioural rules into 4 bits:
– Context
– Scope
– Antecedents
– Results
• Since this, I argue, seems to align with human
cognitive structure
• Which are each dealt with and updated in
different ways (making their use feasible)
• And thus might be a more “natural” structure
for human behavioural rules

Different Aspects I


Different Aspects II

Universe of Knowledge

Knowledge indicated by current cognitive context

Knowledge that is possible to
apply given circumstances

Cause1 & Cause2… 
Result1 & Result2…

Cause3 & Cause2… 
Result5 & Result2…


Bit 1:

Context


A (simplistic) illustration of context from the
point of view of an actor


Situational Context

• The situation in which an event takes place
• This is indefinitely extensive, it could include
anything relevant or coincident
• The time and place specify it, but relevant
details might not be retrievable from this
• It is almost universal to abstract to what is
relevant about these to a recognised type
when communicating about this
• Thus the question “What was the context?”
often effectively means “What about the
situation do I need to know to understand?

Cognitive Context (CC)

• Many aspects of human cognition are context-
dependent, including: memory, visual perception,
choice making, reasoning, emotion, and language
• The brain somehow deals with situational context
effectively, abstracting kinds of situations so
relevant information can be easily and preferentially
accessed
• The relevant correlate of the situational context will
be called the cognitive context
• It is not known how the brain does this, and
probably does this in a rich and complex way that
might prevent easy labeling/reification of contexts


The Context Heuristic

• The kind of situation is recognised in a rich,
fuzzy, complex and unconscious manner
• Knowledge, habits, norms etc. are learnt for
that kind of situation and are retrieved for it
• Reasoning, learning, interaction happens with
respect to the recognised kind of situation
• Context allows for the world to be dealt with by
type of situation, and hence makes
reasoning/learning etc. feasible
• It is a fallible heuristic with social roots in terms
of the coordination of action, norms, habits

Some Possible Examples of Cognitive
Context?

• Greeting someone you do not know
• A lecture
• An interview
• Being Lost
• Being Socially Embarrassed
• Travelling on a train/bus
• Leaving home to go somewhere
• Accidently bumping into someone you do
not know on the pavement/in the corridor

Some Research Responses to
Context-Dependency

A number of responses:
• Only do research within a single context,
resisting any generalisation
• Only use discursive, natural language
approaches where context is implicitly dealt
with (but also mostly hidden)
• Try to see what (inevitably weaker
knowledge) is general across the various
contexts in what is being studied


Context-Dependency and
Randomness

Lots of
information
lost if
randomness
used to
“model”
contextual
variation


However

• Although Cognitive Context in General
might be hard to identify
• Socially Entrenched Contexts are often
rather obvious
• But one needs to drop the imperative of
looking (only) for abstract and general
theories for behaviour
• Being satisfied with more “mundane” and
context-dependent accounts


Choice and Update of Cognitive
Context

• CC is largely learnt from experienced
situations in a rich and unconscious way
• Occasionally one can realise one has the
wrong context if a lot of the detailed
knowledge it indicates is simultaneously
wrong but which is the right CC is a matter
of recognition from past positive learning
• Once CC is learnt it is very difficult to
change, but new CC can still be learnt


Identifying Context from Narrative
Evidence
• Apart from socially entrenched contexts
(lectures, parties, interviews etc.)…
• …the relevant CC is hard to identify from
narrative evidence because:
– To a large extent, we recognise the right CC for
any text unconsciously and easily
– The CC are learnt in a rich, “fuzzy” manner over a
long period of time by inhabiting them which resists
reification
• This is one of the prime needs: how to “mark
up” the CC behind narrative evidence?


Bit 2:

Scope


About Scope

• By “scope” I mean the reasoning as to which
knowledge is possible given the circumstances
• For example, if all the seats are taken in a
lecture, then the norms, habits and patterns as
to where one sits do not apply
• Reasoning about scope can be complex and is
done consciously
• However once judgements about scope are
made then they tend to be assumed, unless
the situation changes critically

Scope vs. Cognitive Context

• Both scope and cognitive context determine
which knowledge is useful for any particular
situation that is encountered
• However, they play very different roles:
– CC is learnt using pattern recognition over a long
time, but then is largely a „given‟, is almost
impossible to change when learnt, is quick and
automatic and is socially rooted
– Scope is largely reasoned afresh each time, taking
effort to do so, is possible to re-evaluate but only if
needed, and is more individually oriented

Identifying and modelling scope

• Compared to CC, scope is relatively well
studied using formal models of reasoning
– e.g. Updating Markoff/state representations of
causation, non-monotonic logics, causation in
Baysian networks etc.
• Scope plays a relatively explicit part in human
language, sometimes being explicitly stated, at
other times using relatively well understood
rules
– e.g. conversational implicature
• It is often possible to infer participant‟s
judgements as to scope and possibility, when
not explicitly mentioned

Bits 3&4:

(local) Narrative Steps


Encoding Narrative Steps

• *If* CC and scope is identified then, I
hypothesize, the local narrative structure will
be easier to understand, because changing
CC and/or scope can do a lot of the “work” in
expressing/encoding knowledge
• Within CC & scope I suggest a simple basic
structure of sets of statements of the form:
(on the whole) Z follows/followed from A, B…
• A very special case of this is when we say
that: A, B… implies Z or that: A, B… causes Z
• (I will write A, B…Z), where A, B are the
“Antecedents” and Z is the Results

About Narrative Steps

• These might not be crisp but of the nature
More A and B tends to result in more Z
• These are often chained in forwards,
branching or backwards manner to make an
inference or a narrative
• (even quite classical) formal logics and
annotation systems capture these
• Most AI/expert systems encode these, but
rarely touch on scope (that is advanced AI)
and never on Context


Conclusion


CSAR as a bridging structure between
narrative text and behavioural rules

*IF* this structure turns out to be a useful and
“natural” encoding of human narrative
knowledge/expression then two steps are
needed:
1. Techniques to capture/approximate/guess
appropriate Cognitive Contexts and Scope
judgments from Narrative Text
2. AI/Computer science architectures that
make the encoding and use of CSAR
structured knowledge

Towards a Context-Sensitive Structure for Behavioural Rules (Context, Scope, Antecedents, and Results)

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