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end for 17: AppendTo [MATH] BestIndividual [MATH] [MATH] )) [MATH] Elitism 18: return [MATH] 19: end procedure 3.6.1 Commonsense crossover |
We introduce two types of commonsense crossover that are tried in sequence by the variation algorithm. The first type attempts a sub-graph interchange between two selected parents similar to common crossover in standard GP; and where this is not feasible due to the commonsense structure of relations forming the parents... |
3.6.2 Type I Crossover (Subgraph Crossover) Firstly, a pair of concepts, one from each parent, that are interchangeable are selected as crossover concepts , picked randomly out of all possible such pairs. |
For instance, for the parent networks in Figure (a) and (b), bird and airplane are interchangeable, since they can replace each other in the relations CapableOf( [MATH] , fly) and AtLocation( [MATH] , air) |
In each parent, a subgraph is formed, containing: 1. the crossover concept; 2. the set of all relations, and associated concepts, that are not common with the other crossover concept |
For example, in Figure (a), HasA(bird, feather) and AtLocation(bird, forest) ; and in Figure (b), HasA(airplane, propeller) MadeOf(airplane, metal) , and UsedFor(airplane, travel) ; and |
3. the set of all relations and concepts connected to those found in the previous step, excluding the ones that are also one of those common with the other crossover concept. |
For example, in Figure (a) including PartOf(feather, wing) and PartOf(tree, forest) ; and in Figure (b), including MadeOf(propeller, metal) ); but excluding the concept fly in Figure (a), because of the relation CapableOf( [MATH] , fly) |
This, in effect, forms a subgraph of information specific to the crossover concept, which is insertable into the other parent. Any relations between the subgraph and the rest of the network not going through the crossover concept are severed (e.g. UsedFor(wing, fly) in Figure (a)). |
The two offspring are formed by exchanging these subgraphs between the parent networks (Figure (c) and (d)). 3.6.3 Type II Crossover (Graph Merging Crossover) |
Given two parent networks, such as Figure (a) and (b), where no interchangeable concepts between these two can be located, the system falls back to the simpler type II crossover. |
A concept from each parent that is attachable to the other parent is selected as a crossover concept The two parents are merged into an offspring by attaching a concept in one parent to another concept in the other parent, picked randomly out of all possible attachments ( CreatedBy(art, human) in Figure (c). Another po... |
3.6.4 Commonsense mutation We introduce several types of commonsense mutation operators that modify a parent by means of information from commonsense knowledge bases. |
For each mutation to be performed, the type is picked at random with uniform probability. If the selected type of mutation is not feasible due to the commonsense structure of the parent, another type is again picked. In the case that a set timeout of [MATH] trials has been reached without any operation, the parent is r... |
3.6.5 Type I (Concept Attachment) A new concept randomly picked from the set of concepts attachable to the parent is attached through a new relation to one of existing concepts (Figure (a) and (b)). |
3.6.6 Type IIa (Relation Addition) A new relation connecting two existing concepts in the parent is added, possibly connecting unconnected clusters within the same network (Figure (c) and (d)). |
3.6.7 Type IIb (Relation Deletion) A randomly picked relation in the parent is deleted, possibly leaving unconnected clusters within the same network (Figure (e) and (f)). |
3.6.8 Type IIIa (Concept Addition) A randomly picked new concept is added to the parent as a new cluster (Figure (g) and (h)). 3.6.9 Type IIIb (Concept Deletion) |
A randomly picked concept is deleted with all the relations it is involved in, possibly leaving unconnected clusters within the same network (Figure (i) and (j)). |
3.6.10 Type IV (Concept Replacement) A concept in the parent, randomly picked from the set of those with at least one interchangeable concept, is replaced with one of its interchangeable concepts, again randomly picked. Any relations left unsatisfied by the new concept are deleted (Figure (k) and (l)). |
Analogy as a fitness measure For experimenting with our approach, we select analogical reasoning as an initial application area, by using analogical similarity as our fitness measure. |
This constitutes an interesting choice for evaluating our work, because it not only validates the viability of the novel algorithm, but also produces results of interest for the fields of analogical reasoning and computational creativity. |
4.1 Analogies and creativity There is evidence that analogical reasoning is at the core of higher-order cognition, and it enters into creative discovery, problem-solving, categorization, and learning (Hofstadter, 2001 . Analogy-making ability is extensively linked with creative thought and regularly plays a role in cre... |
In addition to literary use of metaphors and allegories in written language, analogies often constitute the basis of composition in all art forms including visual or musical. For example, in classical music, it is highly common to formulate interpretations of a composer’s work in terms of tonal allegories (Chafe, 1991 ... |
In science, analogies have been used to convey revolutionary theories and models. A key example of analogy-based explanations is Kepler’s explanation of the laws of heliocentric planetary motion with an analogy to light radiating from the Sun |
Another instance is Rutherford’s analogy between the atom and the Solar System, where the internal structure of the atom is explained by electrons circling the nucleus in orbits like planets around the Sun. This model, which was later improved by Bohr to give rise to the Rutherford-Bohr model, was one of the “planetary... |
In contemporary studies, analogical reasoning is mostly seen through a structural point of view, framed by the structure mapping theory based on psychology (Gentner, 1983 |
Other approaches to analogical reasoning include the view of Hofstadter ( 1995 of analogy as a kind of high-level perception, where one situation is perceived as another one. Veale and Keane ( 1997 extend the work in analogical reasoning to the more specific case of metaphors, which describe the understanding of one ki... |
Computational approaches within the analogical reasoning field have been mostly concerned with the mapping problem (French, 2002 . Put in a different way, models developed and implemented are focused on constructing mappings between two given source and target domains (Figure (a)). This focus neglects the problem of re... |
By combining our algorithm for the evolution of semantic networks with a fitness measure based on analogical similarity, we can essentially produce a method to address this creativity-related subproblem of analogical reasoning, which has remained, so far, virtually untouched. |
We accomplish this by 1. providing our evolutionary algorithm with a “reference” semantic network that will represent the input to the system; and |
2. running the evolutionary process under a fitness measure quantifying analogical similarity to the given “reference” network. This, in effect, creates a “survival of the fittest analogies” process where, starting from a random initial population of semantic networks, one gets semantic networks that get gradually more... |
In our implementation, we define the fitness measure to take the reference semantic network as the base and the individual whose fitness is just being evaluated as the target . In other terms, this means that the system produces structurally analogous target networks for a given base network. From a computational creat... |
This designation of the base and target roles for the two networks is an arbitrary choice, and it is straightforward to define the fitness function in the other direction. So, if the system would be set up such that it would produce base networks, given the target network, one can then interpret this as the the classic... |
If one subscribes to the “retrieval of a base case” interpretation, since the ultimate source of all the information underlying the generated networks is the commonsense knowledge bases, one can treat this source of knowledge as a part of the system’s memory, and see it as a “generic case base” from which the base case... |
On the other hand, if we consider the “imagination of a novel case” interpretation, our system, in fact, replicates a mode of behavior observed in psychology research where an analogy is not always simply recognized between an original case and a retrieved analogous case from memory, but the analogous case can sometime... |
Considering the depth of commonsense knowledge sources, this creation process is effectively open-ended; and due to randomly performed queries, it produces different analogous cases in each run. This capability of open-ended creation of novel analogous cases is, to our knowledge, the first of its kind and makes our app... |
The random nature of population initialization and the breadth of information in ConceptNet and WordNet virtually ensure that the generated semantic networks are in different domains from the one supplied as the input. However, it is possible to formulate fitness measures that include a measure of semantic similarity i... |
4.2 Structure mapping engine The Structure Mapping Engine (SME) (Falkenhainer et al., 1989 is an analogical matching algorithm firmly based on the psychological structure mapping theory of Gentner. It is a very robust algorithm, having been used in many practical applications by a variety of research groups, and it has... |
An important characteristic of SME is that it ignores surface features and it can uncover mappings between potentially very distant domains, if they have a similar representational structure. |
A typical example given for illustrating the working of SME is the analogy between the Rutherford-Bohr atom model and the Solar System, which we already mentioned. Using a predicate calculus representation, Figure illustrates a structural mapping between these domains. |
Here we make use of our own implementation of SME based on the original description by Falkenhainer et al. ( 1989 and adapt it to the simple structure of semantic networks. |
Using SME in this way necessitates the introduction of a mapping between the concept–relation based structure of semantic networks and the predicate calculus based representation traditionally used in SME applications. |
A highly versatile such mapping is given by Larkey and Love ( 2003 . Given information such as “Jim (a man) loves Betty (a woman)”, one can transform the predicate calculus representation of loves(Jim, Betty) gender(Jim, male) gender(Betty, female) into a semantic network representation by converting predicates into no... |
However, the approach of Larkey and Love ( 2003 requires the creation of ad hoc “relation nodes” for the representation of relations between concepts and the usage of unlabeled directed edges. On the other hand, the existing structure of the commonsense knowledge bases that we interface extensively, mainly ConceptNet, ... |
Due to these reasons, we define a basic list of correspondences between the two representation schemes, where we treat “entities” as concepts, relations as relations, attributes as IsA relations; and exclude functions. Table gives the list of correspondences that we employ in our SME implementation. |
4.3 Results With the implementation of analogical similarity-based fitness measure that we described so far, we carried out numerous experiments with reference networks representing different domains. In this part, we present the results from two such experiments. |
Table provides an overview of the parameter values that we used for conducting these experiments. The selection of crossover and mutation probabilities for a particular application have been a traditional subject of debate in EA literature (Spears, 1992 . Since the foundation of the field, in essence, the arguments hav... |
Thus, we use a crossover probability of [MATH] , similar to the high crossover probabilities typically [MATH] encountered in GP literature (Koza et al., 2003 |
However, unlike the typical GP mutation value of [MATH] , we employ a somewhat-above-average mutation rate of [MATH] Due to the fact that our algorithm is the first attempt at having a graph-based evolutionary model of memetics, this mutation rate is somewhat arbitrary and is dependent on our subjective interpretation ... |
We select a population size of [MATH] individuals, and subject this population to tournament selection with a tournament size of [MATH] and a winning probability [MATH] |
Using this parameter set, here we present the results from two runs of experiment: 1. analogies generated for a network describing some basic astronomical knowledge, shown in Figure ; and |
2. analogies generated for a network describing familial relations, shown in Figure 11 For the first reference base network (Figure ), after a run of the algorithm for 35 generations, the system produced the target network shown in Figure 10 |
The produced target network exhibits an almost one-to-one structural correspondence with the reference network, missing only one node ( mass in the original network) and two relations both pertaining to this missing node ( HasA(planet, mass) and HasProperty(matter, mass) ). The discovered analogy is remarkably inventiv... |
It is an intuitive analogy and leaves us with the impression that it is comparable with the classic analogy between the atom and the Solar System that we mentioned in the beginning of this section. Table gives a full list of all the correspondences. |
For the second reference network (Figure 11 ), in a run after 42 generations, our algorithm produced the network shown in Figure 12 |
The produced analogy can be again considered “creative”, drawing a parallel between human beings and musical instruments. It considers a mother as a clarinet and a father as a drum; and just as a mother is a woman and a father a man, a clarinet is an instance of wind instrument and a drum is an instance of percussion i... |
We should note here that each of these two examples were hand-picked out of a collection of approximately hundred runs with the corresponding reference network, chosen because they represent interesting analogies suggesting possible creative value. It is evidently a subjective judgment of what would be “interesting” to... |
During our experiments, we observed that under the selected parameter set, the evolutionary process approaches equilibrium conditions after approximately 50 generations. This behavior is typical and expected in EA approaches and manifests itself with an initial exponential or logarithmic growth in fitness that asymptot... |
Figure 13 shows the progression of the average fitness of the population and the fitness of the best individual for each passing generation, during the course of one of our experiments with the reference network in Figure , which lasted for 50 generations. We observe that the evolution process asymptotically reaches a ... |
Coinciding with the progression of fitness values, we observe, in Figure 14 , the sizes of individual semantic networks both for the best individual and as a population average. Just as in the fitness values, there is a pronounced stabilization of the network size for the best individual in the population, occurring ar... |
Our interpretation of this phenomenon is that, once the size of the best network becomes comparable with the size of the given reference network (Figure , comprising 10 concepts and 11 relations) and the analogies considered by the SME algorithm have already reached a certain quality, further increases in the network s... |
In general, our experiments demonstrate that, combined with the SME-based fitness measure, the algorithm we developed is capable of spontaneously creating collections of semantic networks analogous to the one given as reference. In most cases, our implementation was able to reach extensive analogies within 50 generatio... |
Conclusions We presented a novel graph-based EA employing semantic networks as evolving individuals. The use of semantic networks provides a simple yet powerful means of representing pieces of evolving knowledge, giving us a possibility to interpret this algorithm as an implementation of the idea of memetics. Because t... |
We make extensive use of commonsense reasoning and commonsense knowledge bases, necessitated by the semantic network-based representation and the requirement that all operations should ensure meaningful conceptual relations. Put another way, we use a combination of random processes constrained by the non-random structu... |
For evaluating the approach, we make use of SME as the basis of a fitness function that measures analogical similarity. With the analogical similarity-based fitness calculated between the reference network and the evolving networks in the population, we create a system capable of spontaneously generating networks analo... |
5.1 Limitations and future work The most considerable limitation of this work comes from our choice of using semantic networks instead of a more powerful representation scheme. For example, since we are using SME for experimenting with our approach, it would be highly desirable and logical to use predicate calculus to ... |
This choice of limiting representation was mainly directed by our reliance on ConceptNet version 4 as the main commonsense knowledge base used in this study, which is based on simple binary relations using a limited set of relation types. This impedes the representation of more complex information such as temporal rela... |
Another issue in the current study is the selection of parameter values for our EA implementation. Due to the fact that our algorithm is a first attempt at having a graph-based implementation of memetics, we are faced with selecting mutation and crossover rates without any antecedents. Even in theoretical studies of cu... |
For future work, it would be interesting to experiment with extensions of the simple SME-based fitness measure that we have used. As semantic networks are graphs, a straightforward possibility is to take graph-theoretical properties of candidate networks into account, such as the clustering coefficient or shortest path... |
Another highly interesting prospect with the EA system would be to consider different types of mutation and crossover operators, and doing the necessary study for grounding the design of such operators on existing theories of cultural transmission and variation. Combined with realistically formed fitness functions, one... |
Besides the “memetic” interpretation, a more hands-on application that we foresee we can achieve in the short-term is practical computational creativity. Already with the SME-based fitness measure that we demonstrated in this article, it would be possible to create systems for tasks such as story generation based on an... |
Acknowledgements. This work was supported by a JAE-Predoc fellowship from CSIC, and the research grants: 2009-SGR-1434 from the Generalitat de Catalunya, CSD2007-0022 from MICINN, and Next-CBR TIN2009-13692-C03-01 from MICINN. We thank the three anonymous reviewers whose input has considerably improved the article. |
# Source: arxiv 1407.5719 # Title: Artificial Life and the Web: WebAL Comes of Age # Sections: all # Downloaded: 2026-03-03T01:55:08.110961+00:00 |
Artificial Life and the Web: WebAL Comes of Age Abstract A brief survey is presented of the first 18 years of web-based Artificial Life (“WebAL”) research and applications, covering the period 1995–2013. The survey is followed by a short discussion of common methodologies employed and current technologies relevant to W... |
Introduction Four years ago, in 2010, I clicked a link to watch the new video for the band Arcade Fire ’s latest release, We Used to Wait . Five minutes later, I was sure that what I had just witnessed would change the face of Artificial Life research. This was no ordinary video, but an interactive, localised, personal... |
The Wilderness Downtown On top of the sheer impressiveness of the tightly integrated audio track and visuals based upon Google Street View images of any address entered by the user at the start of the experience, real time animation composited directly over the Street View |
images and guided by the detection of streets within the view, together with some deft control of action shifting between different browser windows, The Wilderness Downtown features some A-Life related technologies such as flocking and procedural content generation. |
It graphically illustrates the potential of the Web as a platform for A-Life applications, and I felt sure when I first watched it that within the next year or two we would be seeing a great deal of this kind of work at Artificial Life conferences. However, that hasn’t happened to quite the degree I was expecting, at l... |
It would appear that, for the time being, commercial development in this area is somewhat ahead of academic work. The rapid development of the Web, and the availability of an ever increasing number of sophisticated APIs, web-focused languages and associated technologies, clearly offers rich potential for developing nov... |
In this paper, I highlight some of the historical roots and early work in this area, some current work, and possible future directions. This is by no means a comprehensive review, but rather just a taster for the breadth, depth, and potential of the field. |
Of particular concern in the following are the new methodologies enabled by web technologies, and the application areas made possible by those methodologies. I will also highlight some currently relevant APIs and technologies, although such things are necessarily rather transient and will doubtless be modified or repla... |
Previous Work Although the latest HTML5 APIs and related technologies offer the possibility of programming sophisticated web applications natively, without the need of plugins or proprietary extensions, the idea of using the Web, or, more generally, the Internet, as a platform for Artificial Life research dates back mu... |
I divide the following review into what I have called WebAL 1.0 and WebAL 2.0 , in very loose analogy to the popular uptake of the term Web 2.0 around 2004–5 (O’Reilly,, 2007 |
WebAL 1.0 In 1995, Tom Ray proposed building a networked version of his well known A-Life system Tierra (Ray,, 1995 . The idea was to use the Internet to create a large, complex environment in which digital organisms could roam and freely evolve. Over a period of 5 years or so, Ray and co-workers used Network Tierra to... |
(Ray,, 1998 The year 1995 also saw the launch of the web-based artificial life virtual world TechnoSphere (Prophet,, 1996 . The front-end of the system was a website where users could design their own creatures by selecting from a limited range of predesigned body parts. Once created, the user submitted their creature ... |
Another early networked A-Life art project was Life Spacies introduced in 1997 and followed by Life Spacies II in 1999 (Mignonneau and Sommerer,, 2001 . This was an interaction environment installed in a museum in Tokyo and connected to a website through which users from all over the world could design virtual creature... |
Verbarium , was also introduced in 1999, and allowed users to create shapes and forms in real-time using the same idea of a text-to-form encoding and an online interactive text editor (Sommerer and Mignonneau,, 1999 |
Moving from art to computer games, the mid-1990s saw the release, in 1996, of the A-Life focused game Creatures The main characters in the game were digital life forms, called Norns that were capable of evolution and lifetime learning, and possessed a physiology, drives, communication abilities, and other life-like ski... |
Norns via enthusiast websites (Jepsen,, 1999 In the following years, two further versions of the game were released, and 2001 saw the release of Creatures Docking Station , an Internet-based add-on to Creatures 3 that allowed Norns to travel between different online worlds. |
A somewhat different kind of A-Life related game was developed by the British design group Soda Creative in 1998. Their system, Soda Constructor , was written in Java and employed a 2D physics engine. It presented users with an online editor with which they could construct creatures based upon mass-spring systems with ... |
Soda Creative won an Interactive Arts BAFTA Award in 2001 for their work. In 2002, they teamed up with Queen Mary University London to develop Sodarace , a shared online environment where users from around the world could pit their creations against each other in competitions. The development of Sodarace was supported ... |
In 2003, Stanley and colleagues initiated development of the computer game NERO , which allowed users to train a team of in-game agents using a real-time version of the NEAT architecture (Stanley et al.,, 2005 . Once trained, the team could be put to battle against an opposing team designed by another (possibly remote)... |
To end this WebAL 1.0 section I take a brief look at some WebAL systems from the online virtual world Second Life , an environment that itself straddled the transition period from Web 1.0 to Web 2.0 . The two most notable projects are |
Svarga and Terminus , both of which first came to prominence in 2006. The first of these, Svarga , was an island with a fully functioning ecosystem comprising a weather system and various types of plants and animals. Shortly after the release of Svarga , a separate effort was launched by the |
Ecosystem Working Group and associated with the in-game location Terminus The group’s aim was a develop an open source programming language that would not only allow developers to freely create their own creatures, but would also allow the creatures in Terminus to interact and evolve using a shared language. Sadly, it ... |
WebAL 2.0 An interesting early WebAL project that explored the potential of distributed computation and native client-side storage was Pfeiffer released in late 2001 (and still running today |
(Langdon,, 2005 . This was a browser-based system that allowed users to evolve 2D patterns described by L-Systems. A user was presented with a variety of patterns on screen, and could select those they thought were good and bad, which directly influenced their evolutionary fitness. The user could also select patterns t... |
One of the first projects to really embrace the potential of multi-user collaboration provided by Web 2.0 technologies was the web-based evolutionary art system Picbreeder |
(Secretan et al.,, 2008 . This is a collaborative interactive evolution that allows users not only to evolve their own images online via the project’s website, but also to continue evolving images produced by other users. Picbreeder thereby allows the evolution of very deep lineages of evolved pictures, and the collect... |
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