Architectures of Memetics & Emergence (part I)

Motivation: autonomous value creating applications

I’ve been working on a very pretentious platform which I hope can prove useful for innovative applications. The platform is based on 2 main principles, Memetics & Emergence. Both are originally taken from the world of human culture & sociology. In the architecture of this platform they are applied to a complex composite Multi Agent System. The motivation behind it is to try mimic the way human individuals, organizations & societies succeed in very large complex tasks, whereas it be a single human, small team, business corporation or a whole society. The fact is that a single human, or any organization of humans is usually good in doing something, called Creating Value. The fact that I earn money is because I create value for my employer; the fact that some company makes money is because it creates value for its customers. So the general motivation for an architecture that tries to mimic human or human organization is to enable software to create value. It isn’t that existing software today doesn’t create value, the only reason software exists is because it creates value. But, unlike software, humans aren’t (explicitly) programmed – they are given some initial knowledge (education/training), they are assigned some jobs, & they create value while collecting the knowledge & expertise in doing it. And this is the motivation. A task such as enabling applications to create value without being programmed seems complete Science Fiction today. So we require something very novel & innovative & something very new to basically be able to claim that we can build such applications, that create value without being programmed, except for some basic education: when assigned with a job, performing it, improving in it & creating value without being specifically programmed as to how to solve each case. The motivation is to create software that just like Humans, even when provided only with basic knowledge of what to do in each case, still can:

• Solve unexpected situations,

• Create value in unexpected ways,

• employ both common sense &

• the ability to learn from situations &

• improve its performance, i.e., the value created,

• by merely performing the job for sufficient amount of time.

 

Memetics & Emergence

So, what are the architecture components that we claim may produce this?

Let’s start with Memetics. Well, memetics basically is the theory that there is an evolution of ideas, where ideas are taken in the broadest sense of things that you copy/learn from others. This evolution is for what is called human culture, science, art, & basically our whole social life is based on memetics. For an ultimate introduction to Memetics, I highly recommend hearing or reading the proponent of this field, Dr. Susan Blackmore. This is the basic idea. This idea can apply not only to the humans world, but also to general intelligent agents. When applied to software, Memetics basically means Evolutionary Knowledge Engineering. The idea is that whereas in knowledge engineering we produce knowledge representations of a domain, including also knowledge required to perform tasks, i.e., Behavioral Knowledge, in Evolutionary Knowledge Engineering, we apply the evolutionary algorithm to this process of knowledge engineering. Meaning that, if we have variations of knowledge representations & if we have different versions of how to perform tasks, only the fittest of these pieces of knowledge will survive & be the base of the knowledge base population. The effect of this is improvement in our knowledge, which becomes more adapted & effective in it’s domain environment. So, to recap, memetics is all about people spreading ideas, & the ideas that are the fittest – most fruitful & valuable – are the ones that survive & base the population of ideas. Similarly, Evolutionary Knowledge Engineering is just Memetics applied not to the culture of humans, but to any society of agents performing knowledge engineering.

 

 

The 2^nd concept called Emergence, is basically a claim that high-level intelligent behavior can be obtained from low-level simple agents, whether it be animals, software or any object with some behavior, when you combine them into a group, that works together. So any time you take a bunch of agents & combine them into a group, even though each of them has a very simple low level behavior, that may not present any intelligence whatsoever, i.e., any complexity, any reasoning behind it, nevertheless, when you combine them into a group, that works together & collaborate, suddenly the group has an higher-level intelligent behavior, in other words, the intelligence emerges from nowhere, by just combining the agents into a bigger unit. For a great introduction to this concept, with numerous eye-opening examples, I highly recommend reading the book on this concept, by Steven Berlin Johnson. Normally, we think of emergence in situations when the intelligent behavior emerges unexpectedly, but I prefer to include any high-level behavior formed by the collaboration of lower-level parts. E.g., a Power Ranger has this amount of power, but when a team of Power Rangers connect together & morph into a giant all-mighty robot, I also see it as emergence. Now this of course may recurse, for example, if you take a group of A-type agents, & combine them into a group, called B-type, & then combine several B-type agents, into a group called C-type. Now the C-type agent can then manifest even higher level of intelligence than B-types agents, & this is like multiplication of the power of emergence, because we start from simple very low-level unintelligent A-type agents, & multiple the emergence effect & get C-type intelligent agents.

Illustration of emergence: combination of many simple pixels into a group, creates complex intelligent picture (Original image by Matt Champlin)

And another illustration: it’s hard to model a 3D shape, e.g.:

 

But if you zoom to a much lower level, you can model the shape, e.g., using many simple triangles:

 

Emergence examples are all around us, everywhere you look, & it’s enough to mention the extreme intelligence (learn & behold) of Ant colonies as a very obvious example. Each ant doesn’t manifest high-level intelligent behavior, but when you combine them into a group, you get a very powerful & successful intelligent behavior. Ants are a very good example, but if you think about it, take any group of humans, whether it be a family, team, community, organization, city, nation, any group of people, is strong because it has more intelligence & more power, that is ability to solve large problems (i.e., Intelligence), only by combining individuals into a higher-level group. In corporations, or hierarchical organizations, we see the emergence multiplication effect, where we take several people into a team, & then take several teams into a department, & then take several department into a division & so forth, we see that more power & more intelligence, more high-level behavior, come out of the group as we multiply the emergence effect. We must understand that it is not the sum of power of the individual components. Take for example a branch of a fast-food chain. The power & intelligence of it, isn’t the sum of the power of the staff running it. The added power & intelligence of these workers isn’t enough to feed thousands of people each day. These young people don’t necessarily understand the process & knowledge, & the sum of their intelligence isn’t enough. Put them all in a room, & you get no special intelligence & power to feed many people. The intelligence is in the fact that working together they create some higher-level machine. They create something that is very powerful, feed thousands of people, but it is not the sum of their intelligence & power. The intelligence is in the combination of them into a collaborating team. Everyone are doing their low-level job, & you get a very powerful higher-level machine. Once they combine you get the emergence effect. Suddenly a bunch of teen-agers feed thousands of people. (This is just an illustration, please don’t take it personal if you happen to be a teen working in a fast-food branch…)

 

 

You could say that both Emergence & Memetics, are nothing but metaphores, ways to see things, which humans have always known. But as any science theory is just a way to see stuff, judged by its fruitfulness in predicting measurements, I believe with these concepts you understand how come human ideas & knowledge improves all the time, & how come the teaming of humans into special types of groups yields so much power, & once you understand it, you can harness this in human life, to create new types of mechanisms, for example as the social services harness the concept of emergence, file-sharing networks, Web2.0 social services, all exemplify it in numerous examples. You can also harness these principles into architecture of software agents, which is what the platform I’ve been working on is all about.

 

Illustration of a simple composite architecture based on Memetics & Emergence

 

 

KQML for SOA

I’ve been using this simple pattern for the past year & a half now, to facilitate SOA, & didn’t yet stop to ponder & evaluate how valuable is it.

KQML stands for Knowledge Querying & Modelling Language, & basically defines a simple message structure, that contains fields like:

• Speech Act
• Recipient
• Sender
• Language
• Content
• Ontology

(there are many more fields, but these are the most important.)

I use KQML together with a distributed shared memory (from GigaSpaces), to create a distributed architecture in which many service providers & applications basically converse with each other in order to achieve almost anything. Any piece of code is devoted to sending & receiving messages, calling services, processing them a bit, & sending them forward.

This allows more complex grid like distribution, using mechanisms that load new service providers according to the accumulation of unhandled messages.

What I learnt using this architecture is that the use of semantic-enabled content (e.g., OWL) made the required message processing much simpler: usually, the content is meaningful to all conversing service providers, so they don’t need to modify it much, just behave according to the requested speech act.

It turned out quite useful & simple for integration with service providers: they usually liked the simplicity of the pattern, requirring them only to know which speech acts are supported & what logical content they need.

Overall, KQML yields quite nice SOA, but it isn’t so common & adopted, because most people turn to well adopted standards (e.g., WS, which can be made semantic using OWL-S). I’m going to start working soon on a new architecture requiring much more dynamic & collaborative conversation, which will put KQML to a real test.

 

Economic models in Multi-agent systems

I’ve read today in the AgentLink newsletter on some new economics-based models in multi-agent systems. Such systems are themselves intended to bring economic value, by performing some task for their human users. Their human users are themselves part of a multi-agent system (some corporation, or organization) intended to bring economic value to some other human users. The whole humansphere is a multi-agent system based on economic models.

The question is: should the multi-agent systems of the artificial agents be based on economic models in order to bring more economic value to their users, or should we apply economic models to the integrated multi-agent system comprising both artifical & human agents?

I tend to believe the 2nd option is more future compliant, although pragmatically people tend to accept the 1st.

 

Finally understood the architecture of the world

The famous programmers quote that “to iterate is human, to recurse, divine” has some good insight. The architecture of the world is simply: recursive multi-agent system. The pattern is so visible & clear: watch every piece of the world & you’ll see many agents, interacting with each other & pursuing their goals. Zoom in & you’ll see that each agent is composed of many smaller agents, that are similarily interacting & pursuing their goals. Zoom out & you’ll see that from the agents interactions & goals pursuing, emerges a higher-level behavior, which turns all of them into a larger agent.

(the great Powers of Ten movie by IBM)

If this is how the world is designed, all the OOP/AOP architectures have a long way until they’ll be able to model anything properly, instead of just stuffing tons of logic into useless huge complexity. Only some Emergence based new architectures (such as Echo & StarLogo) are starting to model the world as it really is.

 

Knowledge syndication

I got as a b-day present 2 great books, one is the must-read On Intelligence, & the other is a billingual collection of Confucius sayings. As there are many knowledge extraction posts on the 1st, e.g. Robert Burke's, but less of the 2nd, I decided I must share with the world the wise Confucius knowledge as a Listible list (the other option was a Moodle course on my homepage).

Then I saw in the browser status bar that my NetVibes tab is busy reading (RSS) content from various great web sites, & it suddenly hit me that instead of me listing Confucius sayings in this location, & Robert Burke listing Jeff Hawkins sayings in that location, what's needed is a standard format for syndicating knowledge beliefs. Not just pure knowledge, domain coneptualization as in the Semantic Web, but practical behavioral knowledge, executable specification, that can be used by beliefs-based engines to acheive goals.

I made recently an architecture of such an engine, that is driven by goals & an inventory of beliefs, but didn't thought about the idea that the inventory of beliefs would be the whole web, or at least what's encoded in the NSSKS format (Not So Simple Knowledge Syndication…).

Maybe it's just a subset/dialect of OWL, but with proper beliefs-based machines, it could be something very powerful. Then we'll need also standards for Goals syndication, & the agents will become completely autonomous, doing the work for us without any need to tell them what, when & how to do it.

 

Kak’s article

Reader of this blog, if you haven’t already, please proceed to read Subhash Kak’s article: Artificial and Biological Intelligence

Some remarks:

• A principle of emergence is self-organizing. Reorganization is a primary process of intelligence.
• the self-awareness of the humanity animal is somewhere else, encoded in a different language & world model.
• an ai could work on the science of understanding humans, as they themselves can’t
• quantum computing theory may explains the brain (remember von Neumann’s quote in Dyson’s article, that logic will have to pseudo-morphose into neuroscience, & not the other way around.)
• “unification of minds or consciousnesses”
• (to be continued)

 

Remarks after reading George Dyson article on Turing, von Neumann & Google

Very cool article: Edge: TURING’S CATHEDRAL by George Dyson

From the perspective of an agents framework’s developer, I have these remarks:

1. ontologies specify behavior. edit the ontology & the software behavior changes. why?
2. create a full corporation of software agents, with hundreds of agent types & thousands of agent workers.
3. the ontology defines the behavior of all agents – their genes, then the ontology also contains the knowledge learnt by the agents – their brain.
4. the key ingredient for agents corporation is communication: normal communication for regular behavior, & spontaneous communication for mutated adaptive behavior.
5. Google is the access method to the global knowledge base of unstructured data. Google should be used extensively by each agent, in order to find new knowledge relevant to its behavior.
6. There is a barrier between machines & humans today: machines can only answer questions that programmers defined well & hard-coded the procedure for answering. most questions people have are not (yet) well-defined & programmers still didn’t define procedures for them. Agentier should be able to communicate directly with people in order to solve “questions whose answers are, in principle, computable, but that, in practice, we are unable to ask in unambiguous language that computers can understand.”
7. Basically this means that a good agent framework should be human brain compatible: it should solve problems that the human brain normally solves.

Quotes:

“An argument in favour of building a machine with initial randomness is that, if it is large enough, it will contain every network that will ever be required.” (Irving J. Good)

“It is much easier to find explicit answers than to ask explicit questions. And some will be answers to questions that programmers wil never have to ask.” (G. Dyson)