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Grounding and the Entailment Structure in Robots and Arti cial Life Erich Prem

[email protected]

The Austrian Research Institute for Arti cial Intelligence Schottengasse 3, A-1010 Wien, Austria Tel. +43 1 5336112, FAX +43 1 5336520 January 1995

Abstract

This paper is concerned with foundations of ALife and its methodology. A brief look into the research program of ALife serves to clarify its goals, methods and sub elds. It is argued that the eld of animat research within ALife follows a program which is considerably dierent from the rest of ALife endeavours. The simulation { non-simulation debate in behavior based robotics is revisited in the light of ALife criticism and Simon's characterization of the sciences of the arti cial. It reveals severe methodological problems, or dangers at least, which can only be overcome by reconsidering naturalness in the study of ALife. Reconsidering Simon's arguments I suugest that ALife is not a science of the arti cial. Furthermore, it is argued that life-as-it-could-be is an ill de ned term, if it is supposed to rescue the research program of ALife into a domain where naturalness is not important. This is so, because it is and must be based on life-as-we-know-it as long as there is no better (necessary and sucient) characterization of life. A comparison of ALife with other such sciences like Arti cial Intelligence and Cognitive Science shows similar problems in these areas, and a similar solution: grounding. The practical upshot of all this lies in the fact that Arti cial Life must indeed be more concerned with natural systems than it may itself consider to have to. Building robots, building models (instead of simulations), and grounding systems may be ways for ALife in the future.

Key words: arti cial life, epistemology, robotics, simulation, symbol grounding

Grounding and the Entailment Structure in Robots and Arti cial Life Erich Prem

[email protected]

The Austrian Research Institute for Arti cial Intelligence Schottengasse 3, A-1010 Wien, Austria Tel. +43 1 5336112, FAX +43 1 5336520 January 1995

Abstract

This paper is concerned with foundations of ALife and its methodology. A brief look into the research program of ALife serves to clarify its goals, methods and sub elds. It is argued that the eld of animat research within ALife follows a program which is considerably dierent from the rest of ALife endeavours. The simulation { non-simulation debate in behavior based robotics is revisited in the light of ALife criticism and Simon's characterization of the sciences of the arti cial. It reveals severe methodological problems, or dangers at least, which can only be overcome by reconsidering naturalness in the study of ALife. A comparison of ALife with other such sciences like Arti cial Intelligence and Cognitive Science shows similar problems in these areas. Grounding, embodiment, and building real models are ways to overcome the dangers of unbounded arti ciality.

1 The Research Program of Arti cial Life

1.1 Goals

A widely accepted list of criteria to be ful lled by ALife has been established by Chris Langton at the Arti cial Life workshop in 1987. He de nes Arti cial Life as the study of man-made systems that exhibit behaviors characteristic of natural living systems. [Langton 89, p.1] :::

According to Langton, ALife (among other things)  tries to synthesize life-like behaviors within arti cial systems,  locates life-as-we-know-it within \the larger context of life-as-it-could-be,"  studies the ongoing dynamic behavior rather than a nal result,  constructs systems which exhibit emergent phenomena  and are not controlled by some central processing unit. 1

natural system

formal domain

encoding

F

N natural law

decoding

inference

Figure 1: Model of a natural system (after [Casti 92, p.29]) Figure 1 serves to make the idea of modeling phenomena of life as it is pursued in ALife more clear. In this gure, N refers to some part of the natural world, e.g. to a group of animals. F denotes some formalism which is supposed to capture some essential characteristics of N , e.g. its behavior, emergent properties, or the dynamics of the system. The encoding of elements from N in F is usually done by a system designer who decides which phenomena of N are encoded in the formalism (e.g. which set of behaviors of an animal and what these behaviors amount to and how they are encoded : : : ). Langton says that the object of study in ALife is to a great extent F , not N ! F is the \man-made system", which ALife studies.

1.2 Methods

The central methodology to ALife research is the construction of artifacts which posses the above characteristics, with a major emphasis on computer simulation. According to the previously mentioned characterization of ALife, its methodology essentially conforms to behaviorism. However, lines of argumentation in ALife papers are very often distinct from a pure behaviorist program. Where behaviorism is rather clear about what is allowed in terms of entities used in explanations, ALifers are usually not so strict. They do recur to ideas which biology has about the generation of certain behaviors, i.e. about internal structures and processes. This methodology can be better understood by depicting it as being metaphor based, as it is done in Fig. 2. natural

formal

selected living system

I-O behavior features

artifact

simulation

I-O behavior (program)

(program)

Figure 2: Metaphor of a system (cf. [Rosen 85, p.81]) 2

Here we have two dierent natural systems: the computer and the living system. The living system is described in the world of formal systems mainly based on its I-O activity, but in ALife practice usually also with respect to internal and external structures (e.g. sensory organs, nervous systems, individuals, etc.). The system designer selects a set of phemonea which she wishes to model. These entities, which comprise at least some kind of input and output, become encoded in the formalism. A computer program then is constructed so as to produce the output from the input. The designer also selects (maybe after the program has terminated or during its run) which entities of the formal system (the program) correspond to entities in the natural system. A characteristic feature of simulations is that not all entities decode into something in the real world. Since there is no direct connection between the system and its simulation, this approach is called a metaphor. Of course, having a real model where all entities of the formal system decode into something about the natural system would be much better. However, the complexity of living system does often not allow to do so; such models as in Fig. 1 are nevertheless the ideal being aimed at.

2 Robots in ALife

2.1 Robotics can be a part of ALife

A considerable part of ALife research is concerned with the construction and programming of robots in order to study behavioral and social phenomena (see e.g. [Resnick 89, Mataric & Marjanovic, Steels 94].) There is a perspective from which it makes good sense to include the study of single behavior-based robots or animats in the domain of Arti cial Life. Proponents of this kind of embodied AI (Arti cial Intelligence) have continually pointed out that a concrete behavior of a robot is created through the interaction of many independent behavioral modules and through the robot's interaction with the physical world [Brooks 91, Steels & Brooks 93]. The great number of interacting modules and their complex way of interaction makes it dicult, if not impossible, to describe an observed robot behavior in terms of the modules which generate it. An observer of such a system is forced to change the level of description from entities which generate the behavior to rules which contain \higher" (behavioral) concepts: an emergent phenomenon has occured. Experiments in which groups of such robots interact and exhibit global phenomena can all the more count as a sub eld of ALife for working without global control. However, in both cases, the problem of inverse emergence or behavior generation (Langton) is a central diculty. The task of generating the correct set and kinds of modules that interact at the right dynamics can be very dicult and time-consuming if one has to build and evaluate all the corresponding robots. This is one of the reasons why the history of robotics is full with computer simulations. However, such simulations have been seriously questioned in the past to an extent which allows to speak about \the simulation { non-simulation debate".

2.2 The simulation { non-simulation debate

In behavior-based robotics it turned out that many relevant aspects of the behavior of agents cannot be simulated for practical reasons. Although it would be theoretically possible to 3

develop an elaborated model of an agent and its environment this is simply not practical. Even if we would succeed in the development of such a model, it is likely that the actual behavior of the robot is in uenced by some properties of the environment which have not been foreseen. Therefore, it is much more practical to actually use a test robot for the evaluation of a theory. As an example for this statement one may consider the simulation of a simple robot which walks around in a room. A full-blown simulation of the robot with the movement of its whiskers, body, legs and its interaction with dierent surfaces amounts to an incredibly dicult piece of software. The physical properties of a robot's body alone are so complicated to calculate that one rather sticks to building the robot in a smaller scale. Note that there are two dierent aspects of this problem here. Firstly, the system consisting of the robot and its environment is very dicult to describe. Secondly, the important characteristics of the robot (those which we actually wish to study) are drastically changed depending on only slight variations of the environment or the interaction of the robot and its environment. This second aspect is it, which is the real argument against any simulation, not the mere diculty of developing a good model. If exactly those features of a system which are subject to non-linear or chaotic in uences are those which we want wo study, then any simulation increases the danger that we simply miss the salient features of the system behavior in a simulation. The term \simulation" is moreover very often used for something that is not a simulation of something at all. The eld of robotics is full with \calculation examples" which are not about anything in the real world. This means that many such so-called simulations are not checked back in the real world, i.e. on the system which they are pretending to actually simulate. But unless this is not done one cannot speak about a simulation. Instead of talking about a simulation it would be better to speak about using a (mathematical) model of some phenomenon instead of the \real" system to predict its behavior. How can you know if you have built such a model if you did not validate it with respect to the real system? The answer is that there is no such thing like the theoretical validation of a model. A model of a natural phenomenon cannot be justi ed by arguments that entirely rely on the formal aspects of it. It can only be shown to be (approximately) appropriate through comparison with what it is supposed to model. (Of course, so-called \simulations" can still serve as important metaphors for the clari cation of ideas. However, this is usually not the purpose with which they are made up and a dierent story not considered in this paper.)

2.3 Entailment in formalisms

The deeper reason for the problems related to this argument about the virtues of simulations lies in the fact that any formalism (by de nition) has only those properties which have been designed in it by some careful engineer (cf. [Rosen 91] or [Pattee 89]). Formal structures are completely arbitrary in what the formalism entails. Physically embodied systems do not share this property with formal models. Such systems are open to interaction (causal dependencies) that go beyond the purely syntactic entailments in any formalism. (\Physics" is not just computation, see e.g. [van Gelder 94] in the context of the question as to whether cognition is computation.) One argument that is often refered to in this debate concerns the confusion about dierent levels of simulation. This argument usually takes the form \We did not simulate this part of the real world, but on a lower level of granularity, we could have done so." Consider again 4

the case of the small robot with its whiskers. It takes a great deal of work and anticipation to model, for example, that the whisker of a robot can be bent or broken. Of course, this can be included in such a simulation, however, it will be done only after a test run with the real robot where the whisker has actually been bent or broken. It is true, that the hypothesis of reductionism is that there exists one formalism which models every natural system. The level at which this formalism operates is, however, too far away from any practically useful model (i.e. it operates below atoms on quarks, electrons, etc.). Let aside the arguments that exist against any reductive theory (see e.g. [Rosen 91]), it follows that appropriate models will always have to be constructed for that special case in which a model is needed. Now the argument of behavior-based roboticists is simply that it is much better not to waste one's time and eorts in the design of complex formalisms which must be validated. Instead, one should use models which automatically contain at least some of the entailment structure of the systems to be modeled.

2.4 Entailment in robotics

This can be done by using physical models, for example. A smaller built vehicle is not a mere mathematical (formal) model of a vehicle. Instead, it is open to physical interactions to which a closed formal system would not be open and it can be assumed that it possesses a great deal of the original system's entailment structure (namely those physical properties which are not aected by the scale). Of course, such physical models do also have to be validated with respect to what these scaling properties are. However, in behavior-based robotics there exists evidence that such physical models are much more likely to model the relevant aspects of a system rather than formal computer simulations which exhibit a great deal of phenomena and characteristics simply not available in the real world and do not possess other features which the real world has. It is worthwile to stress the important dierence between behavior-based robotics (or collective robotics) and the rest of ALife. The mere fact of using a physically embodied robot ensures that a great deal of the characteristics of the model are in accordance with what is being modeled. This means that the metaphor from Fig. 2 changes to what is depicted in Fig. 3. natural

formal

selected living system

I-O behavior features

laws governing robot behavior artifact

behavior

simulation

(robot)

(robot)

Figure 3: Entailment in embodied systems. The model is no longer governed by formal rules alone, but also by material implication, i.e. the same \physics" as the modeled system. 5

In a behavior-based robot the rules governing the system behavior are not completely formal. They depend to a large extent on the physical world and on what physics does to the system. Since the natural system, the behavior of which is actually simulated, is also subject to this very same physics, it can be expected that such an embodied model will automatically contain a large amount of the entailment structure which is governing the original system. In other terms, it will automatically be a better model. As I have argued in the beginning, animat research can be seen as a subarea of ALife. But, apart from that, what has all this concern about material implication, entailment, etc. to do with Arti cial Life, which|after all|is a science of the arti cial ? Is ALife not mainly concerned with F instead of N from Fig. 1? Or, in other terms, what is the object of study in ALife?

3 ALife: A Science of the Arti cal ? 3.1 Simon's sciences of the arti cial

In a widely acknowledged book H. Simon gives an account of what he calls \the science of the arti cial", its subject matter, and the methodology of this area of research. As opposed to the natural scientist who seeks to describe how things are, the scientist of the arti cial is concerned with how things ought to be|how they ought to be in order to attain goals, and to function. [Simon 69, p.7]

An essential characteristic of a science of the arti cial is its concern with construction, i.e. with synthesis. The construction is further aided by the fact that a scientist of the arti cial is mainly interested in imitating appearance, which can be be characterized in terms of functions, goals, and adaptation. For Simon, every adaptive system can also be easily viewed as being arti cial. The reason lies in the fact that adaptivity can be regarded as attaining the goal of being adapted, i.e. it means functioning to some extent. Even more, a factorization of adaptive systems into descriptions of goals and functions would allow us to greatly simplify the description of complex systems. But if function is of central interest to the scientist of the arti cial, he needs mainly be concerned with the I-O behavior of the system: We might look toward a science of the arti cial that would depend on the relative simplicity of the interface as its primary source of abstraction and generality. [Simon 69, p.12]

Simon's book is important to Arti cial Life not only because it is quoted by Chris Langton and others in the eld. Indeed, the characterization above seems to t very well to a great part of ALife. The real value of Simon's contribution lies in his further description of how a science of the arti cial can and should proceed. Simon is one of the rst proponents of simulations as a source of new knowledge, not only in deriving truths about a system which may be implicit in a set of axioms. For Simon, the question as to whether simulation can be of any help to us when we do not know very much initially about the natural laws that govern the behavior of the inner system [Simon 69, p.20]

must be answered positively, and not only so: The more we are willing to abstract from the detail of a set of phenomena, the easier it becomes to simulate the phenomena. [Ibd.]

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As a consequence of these considerations ALife and other similar sciences abstracts from internal structures as long as one succeeds in getting the behavior right. A further consequence is that getting the behavior right can be studied by means of computer simulations. If one refers to Simon (like Chris Langton does) as a proponent of the sciences of the arti cial, one should also point out the dangerous threats of doing so. I have argued elsewhere [Prem 95a] that one of the problems of regarding AI as a science of the arti cial consists in the consequence to consider intelligence as the sum of a set of specialiced domains. This view stems from the need of clearly de ning what is meant by X. Let X be intelligence, cognition, life in AI, Cognitive Science, and ALife, respectively. As soon as we are forced to make explicit the requirements for a technical system achieving X, are we reducing the phenomenon of X to a descriptive list of single aspects of what it means to possess X. The problem is that in a science of the arti cial, which is supposed to support the constrution of a functioning system, this list will automatically be taken to be a complete and sucient speci cation of what it means to be intelligent, cognitive, or alive. Once such an approach has been initiated it cannot be escaped by simply arguing that something would be missing on the list, because each such criticism will in turn support the ongoing process of reduction. Each \turn of the screw" produces another speci cation of \how things ought to be", another research program in a science of the arti cial. This, of course, is a problem only, if one believes that the essence of life, intelligence, and cognition lies in the breadth of the term, i.e. in the innumerable phenomena associated with it. If one thinks, however, that an intelligence test measures intelligence, one may be satis ed with the reductive strategy outlined above. Another of Simon's proposals to cope with complex systems consists in describing (and treating) them as so-called \nearly decomposable systems". Since this characterization is of interest in the present context, it will be brie y described in the next section.

3.2 Nearly decomposable systems

The hierarchical organization of many man-made as well as of natural systems eases, according to Simon, their description very much. In the description and prediction of such systems is suces to deal with decomposable or \nearly-decomposable" systems. We can then distinguish between the interactions among subsystems and the interactions within subsystems (i.e. among the parts of those subsystems). If the intracomponent linkages are much stronger than intercomponent linkages, then the corresponding systems can be easily described by nearly-decomposable matrices. Very often then, these matrices will contain many instances of the same kind of matrix, i.e. it is possible to describe the system as being made up of many parts which possess the same characteristics. This feature is so important for ALife, because in such systems the short-run behavior of subsystems will be approximately independent of other components and the long-run behavior of one component depends only in an aggregate way on other components. Let us compare this feature with what Langton says about the methodology of ALife. Arti cial Life studies natural life by attempting to capture the behavioral essence of the constitutent components of a living system, and endowing a collection of arti cial components with similar behavioral repertoires. If organized correctly, the aggregate of arti cial parts should exhibit the same dynamic behavior as the natural system. [Langton 89, p.3]

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This statement directly follows Simon's claim that [r]esemblance in behavior of systems without identity of the inner systems is particularly feasible it the aspects in which we are interested arise out of the organization of the parts, independently of all but a few properties of the individual components. [Simon 69, p.21]

The research program of ALife is therefore bound with a set of assumptions made about the characteristics of describing and simulating living systems. Again, all these considerations seem to suggest a high degree of abstraction, replacement of a system by its I-O behavior, aggregating building blocks of mainly functional decompositions in order to genereate the desired behavior. Instead of pursuing these theoretical considerations any further here (the reader is referred to a vast amount of corresponding literature in theoretical biology and systems science), I will now argue why there is evidence from a subarea of ALife, i.e. from robotics, that the study of living systems is better not pursued in such a way.

3.3 What ALife can learn from robotics

One of the main goals for switching from more classical robot architectures to the new behavior-based ones was to get the interaction dynamics of the robot right for human purposes. In order to achieve this, two main steps had to be undertaken: (i) physical embodiment of robots as described previously and (ii) replacement of a functional modularization by behavior generating modules [Brooks 85]. Although the separation of functions into modules seemed to be a good idea in robotics, it turned out to be the major hindrance in getting the robot's interaction dynamics with the world right. Research in behavior based robotics provides evidence that getting this interaction speed right is not just some luxurious feature so as to achieve commercially interesting behaviors. The rate of interaction in uences to a very large extent the concrete behavior which can be observed [Smithers 91]. It is true, of course, that in a system comprised of functional modules, these parts will usually only interact at a rather low rate compared to the intracomponent linkage. This is all the more true, if the modules serve to provide complex functions. The kind of behavioral modules in robots, however, do very often interact at high rates and in complex manners. Moreover, they are often fully connected with each other, which can make them a system which is not decomposable into its parts. It is quite obvious that there are a number of diculties associated with viewing functions as decomposable in natural systems, because functions are often not separated in modules with clear interfaces. An example is a bird's wing which is airfoil and engine at the same time [Rosen 93]. But I will not go into this complicated issues deeper here. An additional problem stems from the fact that these robots cannot be judged from a concrete physical environment in which they are put. Only when people started building their robots did it turn out that the architectures which seemed to be the right ones in simulations did not work in the real world. Instead of continuing this criticism I would like to make a positive statement about the possible contribution of behavior-based robotics to ALife. This contribution can consist in recognizing the importance which the automated generation of the right entailment structure in a model has, and how important it can be, in general, to stick to models and not mere simulations. This reacknowledgement of material implication 8

also happened in AI, another science of the arti cial. This development is brie y oulined below so as to justify that Alife is to behavior-based robotics as AI is to grounded connectionism.

3.4 Other sciences of the arti cial

AI is one of the elds explicitely mentioned in [Simon 69], in 1980 Simon extended his set of \sciences of the arti cial" by adding Cognitive Science to it [Simon 80]. The reasons for doing so lie again in the adaptiveness of cognitive systems, in the physical symbol system hypothesis, and in a commitment to a description of human intelligence as a compound of functional modules. It seems quite natural that human intelligence is in a sense of a simple structure, if one assumes low interaction dynamics between the modules. This simply follows from the way in which these modules have been generated, it is an epistemological side-eect, not an ontological one. Let aside all this criticism, one of the hardest attacks on this science of the arti cial is Searles \Chinese Room" argument, which will not be repeated here. Searle attacks symbolic AI on the basis of the speci c character of symbols. Symbols have no other meaning than that which the programmer has given to it through connecting them to other, however again, symbolic description. This diagnosis is, of course, in close accord with the lack of any entailment in formal systems which has not been explicetely designed. In 1990 Stevan Harnad propsed \symbol grounding" as a means for equipping arbitrary symbol tokens with non-arbitrary interpretations [Harnad 90]. He suggests to create some kind of correlational semantics e.g. by means of neural networks to enrich the arbitrary formalism with non-arbitrary meaning. As of today, it seems quite clear that grounding cannot overcome the problems related to intentionality [Prem 95b]. However, grounding can contribute to the problem of narrowing the meaning of symbols, i.e. restricting the possible interpretations of these entities. This is because measurement devices are connected to the neural networks which then extract invariances among the measurements with respect to a given symbol. Thereby, the system's entailment structure becomes directly in uenced by the real world. Symbol gronding can therefore be regarded as the automated construction of formal models of natural domains [Prem 95b, Prem 95c]. A similar argument has long been made by connectionists in discussions which center around so-called \radical connectionism" [Clark 93, p.31]: If networks are \immediately grounded" in the real world, they will be able to take advantage of the features of the real world and provide systems with better representations of the environment. Without the danger over-interpreting these developments in Cognitive Science and AI one can safely say that they show a tendency towards reality not only with respect to the models which are generated. Instead, it is the automated generation of such models which is supported by means of exploiting physics.

3.5 Naturalness Revealed in the Study of ALife

From what I argued in this paper it must be concluded that the science of Arti cal Life is either not a science of the arti cial or that there are severe problems associated with viewing it to be so. I have given several reasons to support this claim, most of which were practical ones drawn from experiences with behavior-based robots. The practical reason follows from the problem of how to generate the right behavior. But this problem is not completely separate 9

from the generation of the correct entailment structure in ALife models. BBR is concerned with reality and it has good reasons to do so: it wants to get the dynamics right. Therefore, it could not remain a pure science of the arti cal. From the fact that ALifers want to study \life as a property of the organization of matter" (Langton) it does not follow that matter need not be considered at all. It can help a great deal, as I have pointed out with the examples above, in getting this organization right. Therefore, as opposed to the claim that the \material is irrelevant" [Langton 89a, p.21], I suggest that matter matters. I shall now give one more analytical reason which follows from a closer look at the notion of \life-as-it-could-be".

4 Could Alife Be a \Life-as-it-could-be"? There seems to exist an argument that can be used to refute the base line of this paper. It is contained in Langton's de nition of ALife research, namely that ALife was not only studying (carbon-based) life on earth, but \life as it could be" [Langton 89a, p.1]. However, ALife has so far undertaken only very few eorts to clarify the meaning and the epistemological status of this deviation in the program of understanding living systems. (\Deviation" with respect to the path of traditional biology.) To me it seems that the research program vaguely speci ed by life-as-it-could-be needs at least some clari cation as to what \life" actually is. Langton and others in the eld try to solve the problem by reference to some phenomenological aspects of living systems like dynamic, chaotic behavior, self-steering processes, directedness, goal-orientation, emergence, : : : Consequently, life-as-it-could-be comprises a wide set of systems that exhibit these phenomena. But then, life-as-it-could-be is only the set generated by a crude extrapolation from rudimentary characterizations of living systems replacing some more serious attempt to answer Schrodinger's still burning question \What is life ?" [Schrodinger 67]. The reasons, however, why such super cial features should be accepted as the essential characteristics of life remain vague. This de nition of life-as-it-could-be is based on life-aswe-know-it, which in turn means that we cannot gain an understanding of the latter from studying the former. More speci cally, reference to some phenomena living systems exhibit cannot explain why a simulation of these phenomena should be called alive. That such nice patterns of dynamic patterns are sucient for attributing life to a system simply does not follow from the fact that systems which have been traditionally called alive exhibit such neat dynamical phenomena. Life-as-it-could-be has always been judged on arguments without any solid ground, except of course, life-as-we-know-it, where its de nition indeed stems from. But this again is something natural. The reason why arti ciality is such a grand enticement to (not only) computer scientists seems to arise from mere despair about the complexities of real life. But, at least to my personal understanding of the scienti c method, desparation is not a scienti c argument.

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5 Conclusion In this paper I have argued along three lines. I have reminded the reader why embodiment of robots, instead of their simulations, turned out to be useful for the eld of robotics. Embodiment serves to produce the right behvior based on the correct entailment structure in models, something which can maybe achieved by grounding, too. Secondly, I have tried to convince the reader why ALife should not be considered what Simon calls a science of the arti cial. Thirdly, I have argued that life-as-it-could-be is an ill de ned term, if it is supposed to rescue the research program of ALife into a domain where naturalness is not important. This is so, because it is and must be based on life-as-we-know-it as long as there is no better (necessary and sucient) characterization of life. The practical upshot of all this lies in the fact that Arti cial Life must indeed be more concerned with natural systems than it may itself consider to have to. Building robots, building models (instead of simulations), and grounding systems may be ways for ALife in the future.

6 Acknowledgements The Austrian Research Institute for Arti cial Intelligence is supported by the Austrian Federal Ministry of Science and Research.

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[Prem 95c] Prem E.: Dynamic Symbol Grounding, State Construction and the Problem of Teleology, paper submitted for publication. [Resnick 89] Resnick M.: LEGO, Logo, and Life, in [Langton 89], 397{406. [Rosen 85] Rosen R.: Anticipatory Systems, Pergamon, Oxford, UK, 1985. [Rosen 91] Rosen R.: Life Itself, Columbia University Press, New York, Complexity in Ecological Systems Series, 1991. [Rosen 93] Rosen R.: Bionics Revisited, in Haken H., Karlqvist A., Svedin U.(eds.): The Machine as Metaphor and Tool, Springer, Berlin, 87{100, 1993. [Schrodinger 67] Schrodinger E.: What is Life and Mind and Matter, Cambridge University Press, Cambridge, UK, 1967. [Simon 69] Simon H.A.: The Sciences of the Arti cial, MIT Press, Cambridge, MA, 1969 (expd.ed. 1980). [Simon 80] Simon H.A.: Cognitive Science: The Newest Science of the Arti cial, Cognitive Science, 4(1), 33{46, 1980. [Smithers 91] Smithers T.: Taking Eliminative Materialism Seriously: A Methodology for Autonomous Systems Research, in: Varela F.J. et al., Towards a Practice of Autonomous Systems, p. 31{40, 1991. [Steels & Brooks 93] Steels L., Brooks R.A.(eds.): The `Arti cial Life' Route to `Arti cial Intelligence'. Building Situated Embodied Agents, Lawrence Erlbaum Ass., New Haven, 1993. [Steels 94] Steels L.: Emergent Functionality in Robotic Agents through On-Line Evolution, in Brooks R.A., Maes, P.(eds.): Arti cial Life IV, Proc. of the Fourth Int. Workshop on the Synthesis and Simulation of Living Systems, MIT Press, 1994.

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