[extropy-chat] Creating software with qualia

[Marc Geddes]

On 12/1/05, "Hal Finney" <hal at finney.org> wrote:
>
> One thing that strikes me about the qualia debate and the philosophical
> literature on the topic is that it is so little informed by computer
> science.  No doubt this is largely because the literature is old
> and computers are new, but at this point it would seem appropriate to
> consider computer models of systems that might be said to possess qualia.
> I will work out one example here.
>
> Let's suppose we are going to make a simple autonomous robot.  It needs
> to be able to navigate through its environment and satisfy its needs
> for food and shelter.  It has sensors which give it information on the
> external world, and a goal-driven architecture to give structure to
> its actions.  We will assume that the robot's world is quite simple and
> doesn't have any other animals or robots in it, other than perhaps some
> very low-level animals.
>
> One of the things the robot needs to do is to make plans and consider
> alternative actions.  For example, it has to decide which of several
> paths to take to get to different grazing grounds.
>
> In order to equip the robot to solve this problem, we will design it
> so that it has a model of the world around it.  This model is based
> on its sensory inputs and its memory, so the model includes objects
> that are not currently being sensed.  One of the things the robot
> can do with this model is to explore hypothetical worlds and actions.
> The model is not locked into conformance with what is being observed,
> but it can be modified (or perhaps copies of the model would be modified)
> to explore the outcome of various possible actions.  Such explorations
> will be key to evaluating different possible plans of actions in order
> to decide which will best satisfy the robot's goals.
>
> This ability to create hypothetical models in order to explore alternative
> plans requires a mechanism to simulate the outcome of actions the robot
> may take.  If the robot imagines dropping a rock, it must fall to the
> ground.  So the robot needs a physics model that will be accurate enough
> to allow it to make useful predictions about the outcomes of its actions.
>
> This physics model doesn't imply Newton's laws, it can be a much simpler
> model, what is sometimes called "folk physics".  It has rules like: rocks
> are hard, leaves are soft, water will drown you.  It knows about gravity
> and the strength of materials, and that plants grow slowly over time.
> It mostly covers inanimate objects, which largely stay where they
> are put, but may have some simple rules for animals, which move about
> unpredictably.
>
> Using this physics model and its internal representation of the
> environment, the robot can explore various alternative paths and decide
> which is best.  Let us suppose that it is choosing between two paths
> to grazing grounds, but it knows that one of them has been blocked by
> a fallen tree.  It can consider taking that path, and eventually coming
> to the fallen tree.  Then it needs to consider whether it can get over,
> or around, or past the tree.
>
> Note that for this planning process to work, another ingredient is
> needed besides the physics model.  The model of the environment must
> include more than the world around the robot.  It must include the robot
> itself.  He must be able to model his own motions and actions through
> the environment.  He has to model himself arriving at the fallen tree
> and then consider what he will do.
>
> Unlike everything else in the environment, the model of the robot is
> not governed by the physics model.  As he extrapolates future events,
> he uses the physics model for everything except himself.  He is not
> represented by the physics model, because he is far too complex.  Instead,
> we must design the robot to use a computational model for his own actions.
> His extrapolations of possible worlds use a physics model for everything
> else, and a computational model for himself.
>
> It's important that the computational model be faithful to the robot's
> actual capabilities.  When he imagines himself coming to that tree, he
> needs to be able to bring his full intelligence to bear in solving the
> problem of getting past the tree.  Otherwise he might refuse to attempt
> a path which had a problem that he could actually have solved easily.
> So his computational model is not a simplified model of his mind.
> Rather, we must architect the robot so that his full intelligence is
> applied within the computational model.
>
> That is not a particularly difficult task from the software engineering
> perspective.  We just have to modularize the robot's intelligence,
> problem-solving and modelling capabilities so that they can be brought
> to bear in their full force against simulated worlds as well as real ones.
> It is not a hard problem.
>
> I am actually glossing over the true hard problem in designing a robot
> that could work like this.  As I have described it, this robot is capable
> of evaluating plans and choosing the one which works best.  What I have
> left off is how he creates plans and chooses the ones that make sense
> to fully model and evaluate in this way.  This is an unsolved problem
> in computer science.  It is why our robots are so bad.
>
> Ironically, the process I have described, of modelling and evaluation,
> is only present in the highest animals, yet is apparently much simpler
> to implement in software than the part we can't do yet.  Only humans,
> and perhaps a few animals to a limited extent, plan ahead in the manner
> I have described for the robot.  There have been many AI projects built
> on planning in this manner, and they generally have failed.  Animals
> don't plan but they do OK because the unsolved problem, of generating
> "plausible" courses of action, is good enough for them.
>
> This gap in our robot's functionality, while of great practical
> importance, is not philosophically important for the point I am going
> to make.  I will focus on its high-level functionality of modelling the
> world and its own actions in that world.
>
> To jump ahead a bit, the fact that two different kinds of models - a
> physical model for the world, and a computational model for the robot -
> are necessary to create models of the robot's actions in the world is
> where I will find the origins of qualia.  Just as we face a paradox
> between a physical world which seems purely mechanistic, and a mental
> world which is lively and aware, the robot also has two inconsistent
> models of the world, which he will be unable to reconcile.  And I would
> also argue that this use of dual models is inherent to robot design.
> If and when we create successful robots with this ability to plan,
> I expect that they will use exactly this kind of dual architecture for
> their modelling.  But I am getting ahead of the story.



See the proposal in my last post.  I suggested not two, but *three*
different kinds of models.  My 'Physical System' corresponded to your
'Physical Model'.  My 'Volitional System' is what you refer to as a
'Computational Model' (which as Jef rightly pointed out is a misnomer - the
physical model is commputational as well).

Did you grok my trick for reconciling the two inconsistent models?  I also
proposed a *third* kind of model ('The Mathematical System') which has the
job of reconciling the other two.  And Qualia/Mathematics emerges from this
third model.


Let us now imagine that the robot faces a more challenging environment.
> He is no longer the only intelligent actor.  He lives in a tribe of
> other robots and must interact with them.  We may also fill his world
> with animals of lesser intelligence.
>
> Now, to design a robot that can work in this world, we will need to
> improve it over the previous version.  In particular, the physics model
> is going to be completely ineffective in predicting the actions of other
> robots in the world.  Their behaviors will be as complex and unpredictable
> as the robot's own.  They can't be modelled like rocks or plants.
>
> Instead, what will be necessary is for the robot to be able to apply his
> own computational model to other agents besides himself.  Previously, his
> model of the world was entirely physical except for a sort of "bubble of
> non-physicality" which was himself as he moved through the model.  Now he
> must extend his world to have multiple such bubbles, as each other robot
> entity will be similarly modelled by a non-physics model, instead using a
> computational one.
>
> This is going to be challenging for us, the architects, because
> modelling other robots computationally is harder than modelling the
> robots' own future actions.  Other robots are much more different than
> the future robot is.  They may have different goals, different physical
> characteristics, and be in very different situations.  So the robot's
> computational model will have to be more flexible in order to make
> predictions of other robot's actions.  The problem is made even worse
> by the fact that he would not know a priori just what changes to make in
> order to model another robot.  Not only must he vary his model, he has to
> figure out just how to vary it in order to produce accurate predictions.
> The robot will be engaged in a constant process of study and analysis
> to improve his computational models of other robots in order to predict
> their actions better.
>
> One of the things we will let the robots do is talk.  They can exchange
> information.  This will be very helpful because it lets them update their
> world models based on information that comes from other robots, rather
> than just their own observations.  It will also be a key way that robots
> can attempt to control and manipulate their environment, by talking to
> other robots in the hopes of getting them to behave in a desired way.
>
> For example, if this robot tribe has a leader who chooses where they will
> graze, our robot may hope to influence this leader's choice, because
> perhaps he has a favorite food and he wants them to graze in the area
> where it is abundant.  How can he achieve this goal?  In the usual way,
> he sets up alternative hypothetical models and considers which ones
> will work best.  In these models, he considers various things he might
> say to the leader that could influence his choice of where to graze.
> In order to judge which statements would be most effective, he uses
> his computational model of the leader in order to predict how he will
> respond to various things the robot might say.  If his model of the
> leader is good, he may be successful in finding something to say that
> will influence the leader and achieve the robot's goal.
>
> Clearly, improving computational models of other robots is of high
> importance in such a world.  Likewise, improved physics models will also
> be helpful in terms of finding better ways to influence the physical
> world.  Robots who find improvements in either of these spheres may be
> motivated to share them with others.  A robot who successfully advances
> the tribe's knowledge of the world may well gain influence as "tit for
> tat" relationships of social reciprocity naturally come into existence.
>
> Robots would therefore be constantly on the lookout for observations and
> improvements which they could share, in order to improve their status
> and become more influential (and thereby better achieve their goals).
> Let's suppose, as another example, that a robot discovers that the
> tribe's leader is afraid of another tribe member.  He finds that such a
> computational model does a better job of predicting the leader's actions.
> He could share this with another tribe member, benefitting that other
> robot, and thereby gaining more influence over them.
>
> One of the fundamental features of the robot's world is that he has
> these two kinds of models that he uses to predict actions, the physics
> model and the computational model.  He needs to be able to decide which
> model to use in various circumstances.  For example, a dead or sleeping
> tribe member may be well handled by a physics model.
>
> An interesting case arises for lower animals.  Suppose there are lizards
> in the robot's world.  He notices that lizards like to lie in the sun,
> but run away when a robot comes close.  This could be handled by a
> physics model which just describes these two behaviors as characteristics
> of lizards.  But it could also be handled by a computational model.
> The robot could imagine himself lying in the sun because he likes its
> warmth and it feels good.  He could imagine himself running away because
> he is afraid of the giant-sized robots coming at him.  Either model
> works to some degree.  Should a lizard be handled as a physical system,
> or a computational system?
>
> The robot may choose to express this dilemma to another robot.
> The general practice of offering insights and information in order
> to gain social status will motivate sharing such thoughts.  The robot
> may point out that some systems are modelled physically and some, like
> other robots, are modelled computationally.  When they discuss improved
> theories about the world, they have to use different kinds of language
> to describe their observations and theories in these areas.  But what
> about lizards, he asks.  It seems that a physics model works OK for
> them, although it is a little complex.  But they could also be handled
> with a computational model, although it would be extremely simplified.
> Which is best?  Are lizards physical or computational entities?
>
> I would suggest that this kind of conversation can be realistically mapped
> into language of consciousness and qualia.  The robot is saying, it is
> "like something" to be you or me or some other robot.  There is more
> than physics involved.  But what about a lizard?  Is it "like something"
> to be a lizard?  What is it like to be a lizard?
>
> Given that robots perceive this inconsistency and paradox between their
> internal computational life and the external physical world, that they
> puzzle over where to draw the line between computational and physical
> entities, I see a close mapping to our own puzzles.  We too ponder over
> the seeming inconsistency between a physical world and our mental lives.
> We too wonder how to draw the line, as when Nagel asks, what is it like
> to be a bat.
>
> In short I am saying that these robots are as conscious as we are, and
> have qualia to the extent that we do.  The fact that they are able and
> motivated to discuss philosophical paradoxes involving qualia makes the
> point very clearly and strongly.
>
> I may be glossing over some steps in the progress of the robots' mental
> lives, but the basic paradox is built into the robot right from the
> beginning, when we were forced to use two different kinds of models
> to allow him to do his planning.  Once we gave the robots the power of
> speech and put them into a social environment, it was natural for them
> to discover and discuss this inconsistency in their models of the world.
> An alien overhearing such a conversation would, it seems to me, be as
> justified in ascribing consciousness and qualia to robots as it would
> be in concluding that human beings had the same properties.
>
> As to when the robot achieved his consciousness, I suspect that it also
> goes back to that original model.  Once he had to deal with a world that
> was part physical and part mental, where he was able to make effective
> plans and evaluate them, he already had the differentiation in place
> that we experience between our mental lives and the physical world.
>
> Hal
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