Artificial Intelligence 60 (1993) 171-183 171 Elsevier

ARTINT 1041

Book Review

Stephanie Forrest, ed., Emergent Computation: Self-Organizing, Collective, and Cooperative Phenomena in Natural and Artificial Computing Networks * P e t e r M. T o d d

The Rowland Institute.lot" Science, 100 Edwin tt. Land Boulevard, Cambridge, MA 02142, USA

Received November 1992

Emergent Computation is a collection of 31 papers from the Ninth An- nual Interdisciplinary Conference of the Center for Nonlinear Studies held at Los Alamos National Laboratory in 1989. As the subtitle indicates, it presents a broad look at the ways that emergent behavior can be employed to process information in natural and artificial systems. This proceedings volume does a better job than most at conveying a coherent picture of a dynamic field. While there are certainly a few papers that will be of interest mainly to specialists, there are also several clear threads that wind through the book and tie together individual papers. Happily, these threads form a web of concepts in a mutually-supporting network. In reading several pa- pers together, emergent phenomena themselves thus come into play: ideas are linked, connections and analogies made, and greater understanding is afforded than in reading these papers in isolation. This is a mark of good editing and selection, for which Forrest is to be commended. In this review, we will cover two of the main threads in this book. The first concerns the

Correspondence to: P.M. Todd, The Rowland Institute for Science, 100 Edwin H. Land Boulevard, Cambridge, MA 02142, USA. E-mail: ptodd@spo.rowland.org.

* (MIT Press, Cambridge, MA, 1991 ); 452 pages, $32.50.

0004-3702/93/$ 06.00 @ 1993 - - Elsevier Science Publishers B.V. All rights reserved

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nature of systems in which emergent computation is possible, and how to get such systems to perform more efficiently. The second thread considers the multiple levels of adaptation necessary to produce adaptive, responsive agents. Each thread has tendrils leading off, further afield, into other papers throughout the volume, as will be indicated.

Forrest begins the volume by introducing the concept of emergent com- putation, defined as an emergent pattern of behavior that is interpretable as processing information. Such behavior can emerge when a number of agents designed to behave in a pre-determined way engage in myriad local inter- actions with each other, forming global information-processing patterns at a macroscopically higher level. From the collective low-level explicitly defined behavior of individuals, the higher-level implicit behavior of the system emerges. Parallel computation processes that take advantage of such emer- gence can be more efficient, flexible, and natural than those that struggle to impose a top-down hierarchical organization on the individual processing agents and their communications. Furthermore, emergent computation may be the only feasible way to achieve certain goals, such as modeling intel- ligent adaptive behavior. But because the components and interactions of emergent computation systems are typically nonlinear, their behavior can be very difficult to control and predict. It is these problems that the papers in this volume set out to address.

Central themes in the realm of emergent computation include:

• self-organization, with no central authority to control the overall flow of computation;

• collective phenomena emerging from the interactions of locally-commu- nicating autonomous agents;

• global cooperation among agents, to solve a common goal or share a common resource, being balanced against competition between them to create a more efficient overall system:

• learning and adaptation replacing direct programming for building work- ing systems; and

• dynamic system behavior taking precedence over traditional AI static data structures.

In all of these ideas, emergent computation dovetails with the "animat path" to simulating adaptive behavior (Wilson [14]) and the behavior- based approach to AI (Maes [8]). The papers in this volume, though, are not solely addressed toward simulating intelligent behavior for autonomous agents. Many of the authors are striving for greater understanding of natural parallel-processing systems such as the brain or the immune system, or for better designs for computer networks. (Forrest divides the papers into the categories of artificial networks, learning, and biological networks.)

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1. The emergence of computation on the edge of chaos

Langton asks a more fundamental question: What are the necessary foun- dations for the emergence of computational abilities themselves? That is, what characteristics does a system need in order to support information transmission, storage, and modification? Langton investigates this problem within the context of cellular automata (CAs), discrete deterministic spatial collections of cells that update their states over time based on the states of their neighbors at the previous point in time. Conway's game of Life (see Gardner [5]) is the archetypal example of a CA system. Langton believes that as abstractions of physical systems, CAs can provide a medium for char- acterizing the requirements of computation in any system. In the context of CAs, such computational abilities take the form of very long chains of CA states. This is because CA patterns that extend over a long range in time and space can store and transmit information, and the complex interactions these transients exhibit can modify that information. These three abilities make up the necessary components of computation.

After giving a brief clear description of CA systems, Langton presents a method for describing a wide range of CAs with a single parameter that rep- resents the bias of the CA's rules toward a particular (arbitrary) state. This parameter can range from a maximum amount of single-state bias (causing the most homogeneous set of CA rules) to a minimum level (causing the most heterogeneous rules). In his studies, Langton varied the parameter over this range and recorded the dynamics of CAs randomly generated with these values. The results were very interesting. Langton discovered an important relationship between this single parameter and the length of transients (and hence support of computation) in the corresponding CAs. Very homogeneous rules created static behavior (the CA settled quickly into a fixed state), while very heterogeneous rules generated random behavior (the CA changed states chaotically). But at a crucial point in between these two extremes, a small range of parameter values yielded CAs with very long transients. Langton likens this crucial parameter range to a phase-transition between the solid (static) CA phase and the fluid (chaotic) phase, and con- cludes that computation is best supported at just such a transitional point. (Conway's Life turns out to be poised at just this point, which accounts for the long dynamic behavior that makes it so interesting.)

This is illustrated very well in the qualitative figures Langton includes in his paper, showing the evolution of states for the different types of CAs. The quantitative analysis of this phase-transition effect is a bit harder to follow (and some terms are left undefined, such as site-percolation). But this analysis, adapted in part from information theory and thermodynamics, presents some useful measures of the complexity and information content inherent in these systems. This exciting work provides strong support for

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the notion that "[c]omplex behavior involves a mix of order and disorder" (p. 32), the type of mix that happens when systems are poised "at the edge of chaos" between the solid and fluid phases. It has wide implications for the nature of computation--for instance, simulated annealing techniques, as discussed in Greene's paper in this volume, can be seen as a way of achieving useful computations by keeping the system "near freezing". However, the important work of developing ways to control (and communicate with) these long complex transients, so that they can be harnessed to do the computing that users want rather than just computing something, remains to be addressed. Finally, even more generally, this work speaks perhaps to the evolution of life itself, via a self-selecting process maintaining itself near a phase-transition point.

2. Fitness landscapes and the requirements for evolvability

In his paper later in this volume, Kauffman addresses a very similar issue from a different perspective, exploring the characteristics necessary for a system to evolve. The process of evolution can create complex structures in a quest for greater and greater fitness in some environment. But in order for evolution to work in a given system, that system must have the property of evolvability, which Kauffman defines as the ability to accumulate successive small improvements in the changing structures. One of the main criteria of evolvability is that the fitness landscape occupied by the evolving structures must be at least partially correlated. This partial correlation makes for a fairly smooth landscape, so that small changes in the structures will typically result in small changes in their fitness. In other words, nearby points in the fitness landscape will have similar fitness values. If this is not the case--if the fitness landscape is completely uncorrelated and rugged--then the small mutations that evolution typically capitalizes on will cause wildly varying changes in the fitness of structures, and no successive set of mutually beneficial mutations will be able to accumulate: evolution will be impossible.

Most systems in nature that exhibit this sort of evolutionary adaptability (including learning and immune responses) are networks of simple pro- cessing agents acting in parallel. Following the lead of this observation, Kauffman investigates the property of evolvability in the artificial setting of Boolean networks. These networks consist of a set of interconnected bi- nary elements, each of which computes a Boolean function of all its inputs from other elements. When these networks are connected randomly and use random Boolean functions, their activity over time consists of a set of deterministic trajectories through binary-vector state space. These trajecto- ries, since they are finite and deterministic, end up either going to a fixed point, or looping endlessly around a state cycle, with the pattern of unit

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activities (on or off) of the system as a whole recurring after some fixed length. Evolvability in such Boolean networks corresponds to small changes in the network (either changes in connectivity, or in the Boolean functions the units use) causing small changes in the behavior of the state cycles. That is, if changing one link in the network results in changing one or two states in one of the state cycles, the network exhibits high evolvability. If one different link causes the complete destruction of several state cycles, the network has low evolvability.

Kauffman has explored several ways of parameterizing the evolvability of Boolean networks. First of all, the connectivity of the network appears to be critical. Networks in which every unit has only two inputs from other units show high evolvability, while those with greater or lesser levels of connectivity show drastically lower evolvability. Another critical parameter is the bias of the Boolean rules used, that is, the percentage of states that map to 1 (or 0), analogous to Langton's rule-bias parameter. As in Langton's CAs, Kauffman has found that there is a critical level of this bias parameter that leads to high evolvability of the Boolean network. This parameter setting too seems to be associated with a phase transition, which shows up quite dramatically in the networks' behavior. For low bias levels, the networks exhibit extremely long state cycles, with each unit's activity essentially changing randomly from one time-step to the next. When visualized as lights (or pixels), the units all seem to "twinkle" on and off. For high bias levels, the majority of the units get stuck, "frozen" either on or off, so that very little activity is seen in the network. But right at the critical value, a web of frozen elements percolates through the network, separating a number of isolated still-active ("melted") areas. In this part-solid, part-liquid state, relatively few, short state cycles are created, each with large basins of attraction. This is what gives these networks their high evolvability: changes (mutations) made to the network can only have a limited local effect, and are stopped from spreading further at the frozen boundaries. All of these properties--small attractor cycles, homeostasis (large basins of attraction and hence resistance to state-changes), and evolvability (highly correlated landscapes )--are linked: "Self-organization for one, bootstraps all" (p. 148).

3. The emergence of symbols in classifier systems

Both Langton's and Kauffman's papers are very elegant, the sort of papers that fill one with excitement and further ideas while reading. The type of work to which these over-arching ideas about the nature of computation and adaptability can be put is well-illustrated in the paper by Forrest and Miller, on emergent behavior in classifier systems. Classifier systems are designed to model intelligent behavior through the action of a large set of

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condition/action rules. The actions of these rules post messages to a com- mon list, and their conditions register the presence in that list of messages from other rules (see the papers by Holland and Farmer for a fuller descrip- tion). The rules are constructed by a process akin to evolution (the genetic algorithm), and their strengths are modified by a credit-assigning learning algorithm (the bucket brigade). Forrest and Miller are interested in the emergence of symbol-processing behavior in classifier systems. What could symbols look like in the context of a classifier system? As the authors see it, symbols in a classifier system could take the form of co-adapted sets of rules, chained together by their messages to each other. Such linked rules can implement default hierarchies, where categorizations of inputs can activate other higher-level categories of greater abstraction. For instance, an input that signifies a "moving object" may turn on a general (default) rule whose output indicates "food", while two other inputs for "red" and "round" may trigger a more specific rule whose output also indicates "food". This second rule overrides the default of the first rule with the inclusion of more specific information (an exception to the default rule). But taken together, the two rules form a higher-level concept (a compound categorization) of "food". and the system should react in the same way no matter how the judgment of "food" was reached. These higher-level representations of concepts can be far removed from the lowest-level messages of the system's input/output interface by many intervening chained messages. Such a picture of symbols, complex entities distributed across several interacting rules, may not seem like the indivisible wholes we often associate with symbolic processing. But their behavioral effects in the system should make them familiar--symbols will be in the eye of the beholder. These compound concepts should allow the classifier system to achieve complex reasoning and efficient encodings of abstract knowledge that a simpler stimulus-response system (with very short rule-chains) could not. (This symbol-grounding problem is addressed in depth by Harnad in his paper, and briefly by Holland in his, as will be mentioned later.) An important question thus arises: Under what conditions will classifier systems exhibit the emergence of long chains of rule activation that may facilitate this type of symbolic processing?

Classifier systems, being complex combinations of several adaptive pro- cesses, have typically not been amenable to such inquiries. But through an insightful mapping from classifier systems to Boolean networks of the sort Kauffman has investigated, Forrest and Miller bring the methods of analysis possible in the latter to bear on the former. In this mapping, classifier sys- tem messages are converted into Boolean network units, so that looking for chains of message-passing classifiers now becomes looking for chains (or cy- cles) of unit activity in the Boolean network. While the results of this study were preliminary at the time, they did provide some interesting insights, indicating for instance the susceptibility of the system to freezing over a

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wide range of conditions. Freezing is to be avoided in this instance, since it means that messages are locked on or off, and hence are doing little useful work. The authors also found that learning classifier systems have difficulty in creating longer rule-chains (at least in one investigated application). But the importance of this work is in creating a bridge between one power- ful, but hard-to-analyze, technique for emergent computation, and another paradigm in which analysis methods have been further developed. Forrest and Miller see benefits passing in both directions across this bridge, aiding the understanding of classifier systems, and spurring on the expansion of analytical results in Boolean networks and other easily quantifiable models of adaptation. Based on this lead, other similar bridges are likely to be built.

4. Speeding co-evolution with parasites

Kauffman is also interested in the dynamics and evolvability of higher- level systems, in particular ecosystems of co-evolving species. In such cases, there is no fixed fitness landscape, as the fitness of one species depends on, and in turn affects, the fitness of other species in the ecosystem. Co- evolutionary systems can show frozen components in the form of species that reach a fitness equilibrium with each other (a Nash equilibrium), being poised at a semi-stable local adaptive peak. These frozen regions will again separate more dynamic portions of the ecosystem in which species continue to evolve with and against one another.

Such ongoing co-evolutionary dynamics are addressed in Hillis' paper on the use of parasites to improve the efficiency of evolutionary search meth- ods. Hillis considers the evolution of the shortest possible sorting networks, lists of test-and-swap actions applied to sequences of values to sort them, a topic of some interest in computer science. He found that by simply evolving the sorters with a fixed fitness function based on the percentage of all possi- ble sorts they performed correctly, a very respectable short sorting network would emerge. (Only binary sequences need to be used to test these sorters, making testing straightforward.) For instance, for 16-number sorters, this scheme found ones that use 65 tests, compared to the current best-known sorter with 60 tests. Hillis used a spatially-organized genetic algorithm with local mating (the implications of which have been explored further more recently by researchers such as Collins and Jefferson [3] ), in which different species of sorters could appear at different points on a two-dimensional grid. (He also uses a diploid gene representation and some other techniques that are confused a bit by a seemingly unrelated set of details in the first section.) However, he noticed that this simple first method included a large amount of wasted computation, for two reasons. First, the fixed fitness landscape induced by the testing method was somewhat rugged, and hence amenable

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to local optima; once a reasonably short sorter was discovered, it was hard to make small changes to it to produce a better sorter. Second, the majority of the sorting tests used to assess a sorter's fitness were unnecessary, since most of the sorters in the population got most of the tests right after just a few generations.

To help solve both of these inefficiencies, Hillis eliminated the fixed fitness function and instead introduced co-evolving parasites, consisting of 10 to 20 test-cases for the sorting networks. These parasites were also distributed over the two-dimensional grid. Their fitness was determined by the number of cases the spatially corresponding sorter failed to get, while the sorter's fitness was the number of cases it did solve. With this competitive fitness scheme in place, the test-case parasites evolved to be as hard for the sorters as possible, while the sorters evolved to do better and better at overcoming the hard cases. The "successive waves of epidemic and immunity" (p. 233) that resulted served to keep the fitness landscape in constant flux and prevent convergence to local optima, while the small size of the parasites avoided wasting computation on unnecessary tests. The sorters that evolved from this co-evolutionary contest were consistently better than those produced by the straight-evolution scheme, getting down to just 61 test-and-swap steps. Thus, the co-evolutionary process that emerged with the introduction of parasites made for the evolution of better sorters in less time. One of the interesting aspects Hillis sees in this work is that the quest for improved optimization techniques like this brings us closer to mechanisms we find in real biological systems.

In the following paper, Ikegami and Kaneko also investigate the nature of co-evolution, creating a model in which to explore the emergence of symbiotic behavior between two species. Their model consists of a set of differential equations specifying the growth relationships between a set of parasites and hosts, represented in an interesting manner as binary strings whose Hamming distance governs their positive or negative effect on one another. They hoped to find situations in which a host-parasite pair would emerge that are beneficial to each other (improving each others' fitness), rather than antagonistic. Such behavior did emerge in some circumstances; however, the fact that the authors build in pairs with such beneficial in- teractions seems to undermine a truly convincing emergence of symbiosis. Furthermore, the strictly periodic behavior their system exhibits does not seem overly natural. This interesting topic calls for more work.

5. A Rosetta Stone between disciplines

A second major thread begins with Farmer's paper, an effort to show the commonalities between many of the computational paradigms discussed

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throughout this book. Farmer's intent is to develop a common underlying language with which to describe connectionist techniques (including neural networks, classifier systems, immune systems, and autocatalytic sets), so that researchers in one field can see analogies in another, and mathematical analysis techniques can be shared across disciplines. The resulting paper is well-written, with straightforward mathematics, and would be a good place to begin reading this volume. Farmer starts by discussing the three time-scales of adaptation in ~'connectionist" systems (this may be a poor choice of a term for all the systems he intends, but we will use it here for consistency). These time-scales can combine synergistically in the creation and behavior of autonomous agents. From shortest time-scale to longest, we have state dynamics, affected by transition rules; parameter or weight values, affected by learning rules; and the structure or architecture of a system, altered by graph dynamics. (In more natural terms, these three would be information processing, learning, and evolution.) The presence of three separate adaptive processes, each operating on a distinct time-scale, distinguishes connectionist systems from other, typically simpler, dynamical systems with change on just a single time-scale, Farmer asserts.

Farmer then goes on to describe each of four different connectionist sys- tems in these general terms, to show their similarities and differences. His discussion of neural network systems as prime examples of connectionism is quite clear, but that for classifier systems is rather too terse and dense, and may not reveal much to someone who doesn't already know about this area (again, Holland's paper presents a better introduction to classifier systems). Models of the immune system are covered next. This area, which has been receiving growing attention (see for example Perelson and Kauffman [10]), has interesting and distinct problems of its own. These include how to recognize self from non-self, how to implement a connectionist system in a fluid (like the bloodstream), which is not amenable to fixed patterns of con- nectivity, and how different forms of memory and information-processing functions can be implemented in a system of immune components (which may not have independently-variable connection strengths). With the rise of epidemics striking primarily at the immune system, this research may also offer insights about a topic of growing importance to autonomous human agents, and any progress that can be made by contact with other connection- ist fields will be valuable. Finally, Farmer considers autocatalytic networks, that is, systems of polymers that stably reproduce themselves. However, these networks may be importantly different from the other systems dis- cussed: while the first three can be seen as adapting toward some external state (i.e. learning the structure of the world, or adjusting to a mix of anti- gens), autocatalytic networks have their own existence as their only goal. This difference may make connectionist analogies to these systems difficult or misguided.

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Farmer briefly mentions several other areas in which the connectionist lan- guage may be useful, including Boolean networks, ecologies, and economics and game theory. He raises a set of questions for further study; some of these have since been answered (for instance, Fahlmann and Lebiere [4] have developed a method for incrementally growing, rather than pruning, neural networks), but others remain important avenues for research. He concludes with a Rosetta Stone for translating various terms and concepts between the different connectionist paradigms, a concise and useful table.

One of the primary problems Farmer poses is the development of meth- ods that can appropriately adapt connectionist systems at all three time- scales. The papers by Wilson, and Schaffer, Caruana, and Eshelman, ad- dress this problem. Wilson uses the genetic algorithm (a search method based on evolutionary principles) to evolve designs for learning percep- tron systems. This approach combines architectural evolution with rule- strength learning and the system's usual state dynamics to adapt over the three time-scales. Wilson finds that the evolutionary search can design rel- atively complex problem-solving structures. But he also reconfirms that the non-specific credit assignment that evolution employs (basing feedback on total lifetime fitness or performance, rather than moment-by-moment success) makes for much slower emergence of efficient computational sys- tems than do methods (like classifier systems or backpropagation neural networks) with more precise multi-level credit apportionment. This work points up the need to evolve not only connectionist structures, but other adaptation methods (e.g. at the level of learning) for finer-grained tem- poral adjustment of those structures as well, as some reseachers (Cecconi and Parisi [2]; Miller and Todd [9]) have begun to explore. Schaffer et al. look at a similar task, evolving neural networks with good generaliza- tion ability. They get interesting results, but with unclear computational efficiency and implementation methods (the details here are not as forth- coming as in Wilson's paper). Their work also seems to miss the point raised by Wilson's paper: it would probably prove more efficient for evo- lution to design learning methods that themselves generalize, rather than architectures that generalize with a given learning rule. Recent research has begun to elucidate just such network methods that promote general- ization ability as they learn (see e.g. Weigend, Rumelhart, and Huberman

[131).

6. Adapting behavior by making predictions

Holland is similarly concerned with the problem of feedback: how can an agent improve its performance in some environment when it only receives very sparse information about how it's doing? One important approach is

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for the agent to develop an internal model of its world, that allows looka- head for behaviors that will lead eventually to rewards. Such models require the ability to form high-level symbolic conceptualizations of low-level input information dynamically, Holland states. This view links Holland's work with that of Mitchell and Hofstadter later in the volume on fluid symbol creation and manipulation in a model of analogy-making, and with Forrest and Miller's interest in the emergence of symbols in classifier systems. At the time, Holland believed that connectionist (that is, neural network model) systems were inadequate for this task, though since then connectionist im- plementations of lookahead and internal models have been developed. (See for example Jordan and Rumelhart [7]. Rumelhart has in fact developed a connectionist implementation of the original Samuel lookahead method for learning checkers that Holland mentions early on in his paper.) Instead, Holland chose to look at the emergence of lookahead and internal models in the classifier systems he developed.

Holland describes a method by which lookahead can be implemented in a classifier system, using messages to propagate virtual action forward in time in a world model formed of rules that capture causal relationships. Chains of triggered rules thus predict what possible rewards lie down different paths of action. By using these predicted rewards to adjust the virtual strength of possible competing current choices, an appropriate action can be taken. The messages used (or portions of messages, called tags) could become protosymbols "associated with external, manipulatable patterns (physical symbols)" (p. 194), leading to a natural symbol grounding (cf. Harnad's paper). These are the same kind of emergent symbols that Forrest and Miller seek in classifier systems, and should similarly be associated with complexes of rules that abstract and generalize from the basic input categories of the system to create new higher-level categories. (The question of what initial low-level categories should be supplied by the input/output interface to the external world is also an important one; Cariani [1] and Todd and Wilson [12] have begun to address this question in evolutionary systems.) Holland hoped that such symbol-based predictive models would emerge on their own in classifier systems, but did not himself achieve this goal. Riolo [11] has since developed a working system that uses an internal model and lookahead to choose its actions, but this area is still very open for further research.

One of the first papers in this volume bridges the two threads considered here: Kephart, Hogg, and Huberman's investigation of the behavior of a system of co-evolving predictive agents. This work comes out of the authors' study of computational ecosystems, in which a set of agents (like computer processes) choose among a collection of resources (like memory access or hardware accelerators) according to their perceived payoffs (like access time). These expected payoffs depend on what the other agents in the system are also doing, and may be erroneous; that is, individual agents

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may figure wrong about the resource with the best payoff, and thus make the wrong decision about which resource to choose. A primary source of expectation error is the time delay between assessing the current state of the system and making the move to use a particular resource: resource R may be free at time T, but by the time the agent decides to use it at time T + 1, it may be swamped with other competing agents. If agents simply choose to ignore this variable time delay, a variety of global behaviors can emerge in the system, including persistent oscillations and even chaos in the assignment of agents to resources (both of which seriously decrease the overall fitness of the system). Thus, some means of taking this information delay into account--that is, predicting the behavior of the system--should help the agents and their decisions.

Kephart et al. consider three types of predictive agents. The first, technical analysts, operate on knowledge of past system performance and use linear extrapolation or cyclical trend prediction to estimate future system perfor- mance. But such agents can push the system into even worse oscillations, or, if successful in small numbers, bring on chaos when they dominate the population. System analysts use a model of the preferences of other agents and how they can affect the system dynamics. If they begin with incorrect assumptions about the perceptions and utility judgments made by other agents, though, they can drive the system to an equilibrium far from optimal. The best performance was attained by combining these two approaches. This resulted in adaptive system analysts that were sensitive to the behavior of the system and changed their internal model in accordance with errors in their predictions. Thus, not taking the behavior of others into account (technical analysts) or making incorrect assumptions about that be- havior (system analysts) can lead to poor system-wide performance. Adding prediction-correcting adaptation of this internal model, though, allows rapid system convergence to a nearly-optimal cooperative solution. As the authors have discussed elsewhere [6], these studies can reveal not only insights into the behavior of living biological agents, but also new methods for designing more responsive computer networks and systems.

7. Concluding remarks

Two other main threads that run through Emergent Computation are descriptions of various learning methods (including a stochastic delta rule, geometric learning algorithms, and parallel simulated annealing), and models of biological neural networks (including selectionist models, theories of visual processing, and computation within the cytoskeleton of individual neurons). Many of these papers are of a more specialized nature, and will appeal primarily to researchers in the particular fields involved. But overall,

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there is enough of a critical mass of papers with more general insights in this volume, and a high degree of coherence and connection between them, to recommend it to a wide audience interested in the nature of adaptive behavior and computation, and their emergence through low-level interactions in highly parallel systems.

References

[1] P. Cariani, Emergence and artificial life, in: C.G. Langton, C. Taylor, J.D. Farmer and S. Rasmussen, eds., Artificial Life II (Addison-Wesley, Reading, MA, 1992) 775-797.

[2] F. Cecconi and D. Parisi, Neural networks with motivational units, Proceedings Second International Conference on Simulation of Adaptive Behavior (Mit Press, 1993).

[3] R.J. Collins and D.R. Jefferson, The evolution of sexual selection and female choice, in: F.J. Varela and P. Bourgine, eds., Toward a Practice of Autonomous Systems: Proceedings of the First European Conference on Artificial Life (MIT Press/Bradford Books, Cambridge, MA, 1992) 327-336.

[4] S.E. Fahlman and C. Lebiere, The cascade-correlation learning architecture, in: D.S. Touretzky, ed., Advances in Neural Information Processing 2 (Morgan Kaufmann, San Mateo, CA, 1988) 524-532.

[5] M. Gardner, Mathematical games: the fantastic combinations of John Conway's new solitaire game 'Life', Sci. Am. 223 (1970) 120-123.

[6] B.A. Huberman, ed., The Ecology of Computation (North-Holland, Amsterdam, Netherlands, 1988).

[7] M.I. Jordan and D.E. Rumelhart, Forward models: supervised learning with a distal teacher, Cogn. Sci. 16 (1992) 307-354.

[8] P. Maes, Behavior-based artificial intelligence, Proceedings Second International Conference on Simulation of Adaptive Behavior (Mit Press, 1993).

[9] G.F. Miller and P.M. Todd, Exploring adaptive agency I: theory and methods for simulating the evolution of learning, in: D.S. Touretzky, J.L. Elman, T.J. Sejnowski and G.E. Hinton, eds., Proceedings 1990 Connectionist Models Summer School (Morgan Kaufmann, San Mateo, CA, 1990) 65-80.

[10] P.S. Perelson and S.A. Kauffman, Molecular Evolution on Rugged Landscapes: Proteins, RNA and The Immune System (Addison-Wesley, Reading, MA, 1991 ).

[ 11 ] R.L. Riolo, Lookahead planning and latent learning in a classifier system, in: J.-A. Meyer and S.W. Wilson, eds., From Animals to Animats: Proceedings of the First International Conference on Simulation of Adaptive Behavior (MIT Press/Bradford Books, Cambridge, MA, 1991) 316-326.

[12] P.M. Todd and S.W. Wilson, Environment structure and adaptive behavior from the ground up, in: Proceedings Second International Conference on Simulation of Adaptive Behavior (Mit Press, 1993).

[13] A.S. Weigend, D.E. Rumelhart and B.A. Huberman, Generalization by weight-elimination with application to forecasting, in: R.P. Lippmann, J.E. Moody and D.S. Touretzky, eds., Advances" in Neural Information Processing 3 (Morgan Kaufmann, San Mateo, CA, 1991 ) 875-882.

[14] S.W. Wilson, The animat path to AI, in: J.-A. Meyer and S.W. Wilson, eds., From Animals to Animats: Proceedings of the First International Conference on Simulation of Adaptive Behavior (MIT Press/Bradford Books, Cambridge, MA, 1991 ) 15-21.