Reading � Curious machines

Greg Detre

Sunday, March 30, 2003

 

Norman, Ortony and Russell (2003), �Affect and machines design: lessons for the development of autonomous machines�, in IBM Systems Journal

cognition � �interpretet, understand, reflect upon, and remember things about the world�

affect � �rapidly evaluates events to provide an initial assessment of their valence or overall value with respect to the person�

�positive or negative, good or bad, safe or dangerous, hospitable or harmful, desirable or undesirable etc.�

they mention Damasio�s evidence that patients with neurological damage to their affective systems are unable to operate on a day-to-day basis, despite appearing to perform normally on standardised cognitive tests

affect as essential for intelligent behaviour by altering goal priorities and generating interrupts

they�re thinking about systems that have a variety of tasks + goals, must run attended and autonomously, and need high reliability

three levels:

reaction, routine, reflection

processing at each level serves both a cognitive and affective function

higher levels: greater depth and slower processing

control information (activation/inhibition) flows downwards

Reaction

lowest level, perhaps genetically determined in animals

immediate responses to state information coming from the sensory systems � function is rapid reaction to the current state

fast, hard-wired detectors

interrupts higher-level processing

responses at the reaction level can be potentiated or inhibited by inputs from these higher levels, and they can habituate, reducing sensitivity to expected signals

Routine

skilled and well-learned, largely �routinised� behaviours

this is the home of most motor skills, incl language generation

considerable processing to select and guide behaviour

control signals from above (inhibition and activation), it can both inhibit and activate reaction level responses and can pass affective information up to the reflection level when confronted with discrepancies from norms or routine expectations

performs assessment � values on three dimensions

positive

negative

(energetic) arousal

default expectations � when these aren�t met, the system can make adjustments and learn

Reflection

mind deliberating about itself � performs operations upon its own internal representations of its experiences, its physical embodiment, its current behaviour, and the current environment, along with the outputs of planning, reasoning and problem-solving

input only from lower levels and neither receives direct sensory input nor is capable of direct control of behaviour

interrupts from lower levels can direct and redirect Reflection-level processing

don�t see how this fits with the downward-only control???

negative affect

vigilance

especially with high arousal appears to lead to more focused an deep processing

positive affect

curiousity

broad, more widely spread processing

humans have more enhanced creativity in a pleasurable state

current systems do not distinguish between affect (evaluation) and cognition (understanding)

the 3 levels in artificial systems

alarms

deviations from norms

restructuring queues, priorities or resource allocation

e.g. virtual memory in modern OSs

considers the example of a RAID system that starts to take precautions, and prioritise extra reliability over performance when it becomes �anxious�

�anxiety� here amounts to the system�s appraisal of its own trustworthiness

important question:

why are they proposing the introduction of affect as a whole new system, rather than just writing appropriate algorithms for each potential problem?

strong methods:

exploit specific domain knowledge + structure

weak methods + heuristics:

designed to be much more general, making them slower, less efficient and not guaranteed to succeed � trade efficiency for generality (e.g. hill climbing)

strong methods are always preferable when you know the situation,the environment is predictable and limited in scope � otherwise you have to rely on weak methods (just as biology employs (in combination with strong methods, e.g. hard-wiring) to cope with an uncertain, world) � affect is just such a weak method

 

 

Points

only 3 dimensions of affect???

where do they fit with Ekman�s 6???

is there a way of breaking them down further, or are they orthogonal to human emotions???

cf autonomic computing� references???

isn�t an entirely downward control information flow very rigid/awkward??? how does it deal with interruptions???

 

 

Sloman (2001), �Varieties of affect and the CogAff architecture schema�

3+ reasons for the interest in computer models of emotions

1.       an interest in emotions s something to be modelled + explained

2.       giving machines an understanding of emotions in order to make them better for HCI

3.       new kinds of computer entertainments that employ convincing emotional behaviour

architecture-based concepts:

�starting with specifications of (virtual machine) architectures for complete agents and then finding out what sorts of states and processes are supported by those architectures�

e.g. thrashing and deadlock (i.e. time spent swapping and paging virtual memory) as architecture-based concepts

�defined in terms of causal interactions between states and processes within mechanisms in a virtual machine architecture, and in that sense they involve a functional perspective�

in contrast with philosophical functionalism, which �defines mental states in terms of the relationships between inputs/outpus of the whole system without mention of the internal architecture�

various definitions of emotion emphasise:

brain processes

peripheral physiological processes

patterns of behaviour

eliciting conditions

functional roles

introspective qualities

some include all motives + desires (e.g. hunger and curiosity), others include surprise (or treat as just a �cognitive state in which a belief expectation has been found to be violated, which may or may not produce an emotional reaction�)

emotions are rich in semantic content + direction (e.g. �being angry with a particular person about a particular action performed by that person etc.), as opposed to continuously variable global state

should not necessary represent intensity of emotions as single numerical variables � the intensity may be emergent, like the degree of thrashing

�important not to assume that the forms of representation that are useful � to use when describing a complex system or predicting its behaviour are to be found in the system itself�

ways to deal with these conceptual confusions:

ignore

seek operational definitions of various states in terms of measurable aspects of behaviour

do surveys of linguistic usage

study the role of emotions in literature

conceptual analysis � iterations of conjecture, testing, modification

try and define different emotions in terms of their biological functions (and adaptiveness)

but some, e.g. grief + embarrassment might be emergent states

CogAff schema

pillars

perception

central processing

action

layers

reactive mechanisms

deliberative reasoning

meta-management (reflective processes)

alarms

purely reactive and pattern-driven, stupid, capable of mistakes, may be trainable

the nature of each component in the 3x3 grid is defined by its functional connections to all the others

architecture-based concepts (e.g. of pain) make the concepts precise, and allow questions to be formulated

reactive systems

lack the ability to represent, evaluate and compare possible actions + their consequences (i.e. counterfactuals, relativity of time + place)

combinatorial explosion

normal vs threat/opportunity instances � proto-emotions

where different kinds of needs dominate innate processing � proto-motives

more global states, less goal-directed but change the quality of processing in some general way � proto-moods

deliberative

able to �represent, analyse, compare, evaluate and react to descriptions of hypothetical future scenarios or possible explanations of previously observed phenomena�

use structured representations with a compositional semantics

it may not be possible to tell reactive proto-caution from deliberative proto-caution from the outside, but only by looking at the internal processing architecture

meta-management

�self-observation or self-monitoring of a wide variety of internal states, along with categorisation and evaluation of those states, linked to high-level mechanisms for learning and for controlling future processes�

higher-order emotions

depend on which layer they relate to

 

Points

virtual machines???

i.e. multiply realisable???

swapping vs paging???

the difficulty of categorising surprise (and indeed the motivational states) is that they can�t be cleanly differentiated from cognitive

surely, different numbers of layers exist(ed) in different modules, depending on complexity, need + ecological niche � they needn�t/wouldn�t have emerged complete/distinct

is there a clean distinction between reactive + deliberative???

both need a kind of LTM

hypothetical scenarios though�

how is CogAff a space of architectures???

he used �qualia�! presumably just to mean sense-data though�

more than 3 (levels???)??? nonsensical to abstract this far???

ignores the �hard problem�

do Sloman�s levels correspond to Norman et al.�s 3, and do they correspond to Minsky�s A-, B- and C-brains???

what do Minsky�s extra 3 add???

To do

look at Minsky�s 6-level model

look at �what is it like to be a rock� again

look up references

 

Minsky (unpublished), �The emotion machine�, ch 2, �Attachment and goals�

Notes

Playing with mud, attachment and goals, Imprimers, the Attachment Elevator hypothesis

when a stranger rebukes us, or when we experience negative reinforcement for some action, we alter our methods

pride + shame � �attachment-based learning�

attachments help us learn new kinds of goals

�in the face of a parent�s blame or reproach she learns that her goal was not good to pursue�

�when we�re praised or rebuked by the people we love, we don�t just feel pleased or dissatisfied; instead, we tend to feel proud or ashamed�

the way we have evolved to deal with living in complex communities that potentially vary enormously (in climate, culture, language, lifesteyle etc.) is to rely less on survival skills that we possess from soon after birth, and more on learning from those around us

cf reputation-based appraisal systems, which have to solve the similar problem of sifting through an enormous amount of data in real-time, evaluate it coarsely then at a more fine-grained level, but using algorithmic processes � you start by conjecturing who�s worth listening to (moderators), then you pay attention to their evaluations, then you reevaluate your moderators

for this reason, our parents� and elders� views are even more important, not just because we are so dependent on them as children, but because of the need to �download� their wisdom

other theories: smaller heads to facilitate childbirth, or that huge brains just take longer to learn � he thinks we mature in well-spaced stages to make multiple levels of representation

�it is one thing to learn how to get what you want � and another, to learn what you ought to want�

the popular theory about children building their characters during their formative years doesn�t answer:

to whom do our children become attached?

how does attachment help establish our values?

what is the span of those �formative� years?

when, if ever, do we outgrow them?

Goal-Cloud model � when a goal is satisfied by some action, then you connect the goal to the sub-goal of achieving that action (or avoiding some action that didn�t work)

but how do you learn new high-level goals, i.e. values?

cf Aristotle rhetoric quote about shame

doesn�t like �caregiver� as a term for people you�re attached to, because the attachments can form without physical care

Imprimer: a child�s Imprimer is one of those persons to whom that child is attached

Impriming: a special way to learn new goals that works when a child�s Imprimer is present

he�s happy to admit that there�s no neuroscientific evidence for Impriming, but that there hasn�t been much of a search for it

pride from Imprimer praise elevates her present goal to a higher kind of priority

talks about controlling the �level of detail�

Learning

3 types of learning when Carol plays with mud:

aversion learning � when the stranger scolds her, she learns to avoid such situations

attachment learning � devalues her current goal when scolded by mother

subgoal (�reinforcement�) learning � when succeeds by using an action, the child learns that this is a useful subgoal

learning is a suitcase word

which aspects of what hse happened to do should take credit for her final success?

�Which features of those recent events should Carol�s brain decide to record?

Should she record where they occurred, or which other persons were present?

Should she remember which shoes she wore or whether the weather was cloudy or clear?

Which of the thoughts she was thinking then should be included in that description?

Where will she store the records she makes�and how will she later retrieve them?

How will Carol represent all those kinds of information? (See Representations.)�

feelings �reflect our attempts to describe complex cascades of reactions�

Formation of conscience and self-ideals

conscience, ideals values: when we use those Imprimer-based emotions on our own to evaluate unfamiliar goals

rants about mysticism, and debunks other theories about where our moral sense comes from

Bowlby: "The variables which determined most clearly the figures to whom the children would become attached were the speed with which a person responded to an infant and the intensity of the interaction in which he engaged with that infant."

the presence and praise of an Imprimer has come to serve as an �innate releasing mechanism� (Tinbergen) for a special system we call the �attachment elevator�

Imprimer problems, Self-models and self-consistency

discusses Imprimer problems

having more than one Imprimer, may become muddled, inconsistent (see point re trusting yourself below)

the danger of having your goals altered by strangers

discusses cult membership

�we must be wary of changing ourselves in ways that prevent us from ever changing again

Bowlby argued that attachments are mainly for physical safety � as opp to food provision

there�s no point being able to put together a 10-step plan unless you�re going to have the future self-consistency to carry it out

�the more you�re able to trust yourself, the more you can simplify yourself � until you become your own caricature� - hmm

see self-caricature point

tells the story of Arthur Samuel�s Checkers program that had to have its learning turned off when playing worse players � that�s because it didn�t know enough about what/why it�s learning�

Public Imprimers

discusses celebrities

rhetoric � �an oration that has the right kinds of timing can seem like a �virtual interaction� by entraining some of the listener�s thoughts, by first raising questions in their minds � and then swiftly and aptly answering them�

 

Points

the self-caricature bit is laid on too thick

�In the course of each person's development, they tend to evolve certain policies that are so systematic and consistent that we (or our friends) can recognize them in the forms of what we call features or traits�and these become parts of our self-images. Then when we formulate our plans, we can use those traits to predict what we�ll do (and thus reject schemes that we know we won't do). Whenever this works we�re gratified, so then we continue to train ourselves to behave in accord with these simplified descriptions. Thus, over time our imagined traits proceed to make themselves actual!�

does he explain how the species creates new ideas???

an element of trial and error, accident, fortuituous misassignment of credit, I spose

For instance, while chimps may be able to discuss counterfactual situations in � , and that this allows us to perform some meta-management level thinking about, say

cf reputation-based appraisal systems, which have to solve the similar problem of sifting through an enormous amount of data in real-time, evaluate it coarsely then at a more fine-grained level, but using algorithmic processes � you start by conjecturing who�s worth listening to (moderators), then you pay attention to their evaluations, then you reevaluate your moderators

 

Minsky, ch 5

�Inborn, Instinctive Reactions: Joan hears a sound and turns her head. Many infant animals do such things; they�re born with just enough �instincts� to help them survive.

Learned Reactions: She sees a car. Joan has learned that certain conditions demand new, specific reactions to them. How does she manage to learn such things?

Deliberative Thinking: She is thinking about what to say at the meeting. Here she imagines alternative futures, and various ways to choose among them. What kinds of resources might work in her brain, to envision and reason about such things?

Reflective Thinking: Joan reflects upon what she has done. To do this Joan must use some internal paths through which her resources can �react� not only to her environment, but also to traces and records of things that recently happened inside her brain.

Self-Reflective Thinking: Uneasy about arriving late. Some monitor watches Joan's temporal progress, and insists that she must not delay her decision�or something bad might happen to her.

Self Conscious Emotions: �What would my friends have thought of me?� Now Joan thinks about what she ought to have done. Did her actions live up to her self-image of how she should have or ought have behaved?�

Discarded

Differences

role of alarms

how integrated the affective component is

how much the low-level does

whether you divide up the high levels

 

Gregory discusses the importance of allowing top-down and bottom-up to intermingle and pressure each other

For instance, while chimps may be able to discuss counterfactual situations in � , and that this allows us to perform some meta-management level thinking about, say

cf reputation-based appraisal systems, which have to solve the similar problem of sifting through an enormous amount of data in real-time, evaluate it coarsely then at a more fine-grained level, but using algorithmic processes � you start by conjecturing who�s worth listening to (moderators), then you pay attention to their evaluations, then you reevaluate your moderators

 

Quotes

see notes � curious machines project 1