The Internet, the World-Wide-Web and hypertext were all forecast by Vannevar Bush, in a July 1945 article for The Atlantic, entitled  As We May Think.  Perhaps this is not completely surprising since Bush had a strong influence on WW II and post-war military-industrial technology policy, as Director of the US Government Office of Scientific Research and Development.  Because of his influence, his forecasts may to some extent have been self-fulfilling.

However, his article also predicted automated machine reasoning using both logic programming, the computational use of formal logic, and computational argumentation, the formal representation and manipulation of arguments.  These areas are both now important domains of AI and computer science which developed first in Europe and which still much stronger there than in the USA.   An excerpt:

The scientist, however, is not the only person who manipulates data and examines the world about him by the use of logical processes, although he sometimes preserves this appearance by adopting into the fold anyone who becomes logical, much in the manner in which a British labor leader is elevated to knighthood. Whenever logical processes of thought are employed—that is, whenever thought for a time runs along an accepted groove—there is an opportunity for the machine. Formal logic used to be a keen instrument in the hands of the teacher in his trying of students’ souls. It is readily possible to construct a machine which will manipulate premises in accordance with formal logic, simply by the clever use of relay circuits. Put a set of premises into such a device and turn the crank, and it will readily pass out conclusion after conclusion, all in accordance with logical law, and with no more slips than would be expected of a keyboard adding machine.

Logic can become enormously difficult, and it would undoubtedly be well to produce more assurance in its use. The machines for higher analysis have usually been equation solvers. Ideas are beginning to appear for equation transformers, which will rearrange the relationship expressed by an equation in accordance with strict and rather advanced logic. Progress is inhibited by the exceedingly crude way in which mathematicians express their relationships. They employ a symbolism which grew like Topsy and has little consistency; a strange fact in that most logical field.

A new symbolism, probably positional, must apparently precede the reduction of mathematical transformations to machine processes. Then, on beyond the strict logic of the mathematician, lies the application of logic in everyday affairs. We may some day click off arguments on a machine with the same assurance that we now enter sales on a cash register. But the machine of logic will not look like a cash register, even of the streamlined model.”

Edinburgh sociologist, Donald MacKenzie, wrote a nice history and sociology of logic programming and the use of logic of computer science, Mechanizing Proof: Computing, Risk, and Trust.  The only flaw of this fascinating book is an apparent misunderstanding throughout that theorem-proving by machines  refers only to proving (or not) of theorems in mathematics.    Rather, theorem-proving in AI refers to proving claims in any domain of knowledge represented by a formal, logical language.    Medical expert systems, for example, may use theorem-proving techniques to infer the presence of a particular disease in a patient; the claims being proved (or not) are theorems of the formal language representing the domain, not necessarily mathematical theorems.

References:

Donald MacKenzie [2001]:  Mechanizing Proof: Computing, Risk, and Trust (2001).  Cambridge, MA, USA:  MIT Press.

Vannevar Bush [1945]:  As we may think.  The Atlantic, July 1945.

Technorati Tags: logic programming, computational argumentation, Donald MacKenzie, Vannevar Bush

People exert large amounts of problem-solving effort playing computer games. Simple image- and text-recognition tasks have been successfully ‘crowd-sourced’ through games, but it is not clear if more complex scientific problems can be solved with human-directed computing. Protein structure prediction is one such problem: locating the biologically relevant native conformation of a protein is a formidable computational challenge given the very large size of the search space. Here we describe Foldit, a multiplayer online game that engages non-scientists in solving hard prediction problems. Foldit players interact with protein structures using direct manipulation tools and user-friendly versions of algorithms from the Rosetta structure prediction methodology, while they compete and collaborate to optimize the computed energy. We show that top-ranked Foldit players excel at solving challenging structure refinement problems in which substantial backbone rearrangements are necessary to achieve the burial of hydrophobic residues. Players working collaboratively develop a rich assortment of new strategies and algorithms; unlike computational approaches, they explore not only the conformational space but also the space of possible search strategies. The integration of human visual problem-solving and strategy development capabilities with traditional computational algorithms through interactive multiplayer games is a powerful new approach to solving computationally-limited scientific problems.”

References:

Seth Cooper et al. [2010]: Predicting protein structures with a multiplayer online game.  Nature, 466:  756–760.  Published:  2010-08-05.

Eric Hand [2010]:  Citizen science:  people power.  Nature 466, 685-687. Published 2010-08-04.

The Foldit game is here.

Technorati Tags: crowd-sourced, Protein structure prediction

For some time, I have been writing on these pages that the currently-fashionable paradigm of the Information Society is inadequate to describe what most of us do at work and play, or to describe how computing technologies support those activities (see, for example, recently here, with a collection of posts here).   Most work for most people in the developed world is about coordinating their actions with those of others  – colleagues, partners, underlings, bosses, customers, distributors, suppliers, publicists, regulators, und so weiter.   Information collection and transfer, while often important and sometimes essential to the co-ordination of actions,  is not usually itself the main game.

Given the extent to which computing technologies already support and enable human activities (landing our large aircraft automatically when there is fog, for example), the InfoSoc paradigm, although it may describe well the transmission of zeros and ones between machines, is of little value in understanding what these transmissions mean.  Indeed, the ur-text of the Information Society, Shannon’s mathematical theory of communications (Shannon 1948) explicitly ignores the semantics of messages!  In place of the InfoSoc metaphor, we need another new paradigm, a new way to construe what we are all doing.  For now, let me call it the Joint-Action Society, although this does not quite capture all that is intended.

I am pleased to learn that I am not alone in my views about InfoSoc.   I recently came across an article by the late Peter Martin, journalist, editor and e-businessman, about the lessons from that great disaster of privatization of Railtrack in the UK.  (In the 1980s and 90s, the French had grand projets while the British had great project management disasters.)  Here is Martin, writing in the FT in October 2001 (the article does not seem to be available online):

Railtrack had about a dozen prime contractors, which in turn farmed out the work to about 2,000 subcontractors.  Getting this web of relationships to work was a daunting task.  Gaps in communication, and the consequent “blame culture” are thought to be important causes of the track problems that led to the Hatfield crash which undermined Railtrack’s credibility.

.  .  .

These practical advantages of wholesale outsourcing rely, however, on unexamined assumptions.  It is these that the Railtrack episode comprehensively demolishes.

The first belief holds that properly specified contracts can replicate the operations of an integrated business.  Indeed, on this view, they may be better than integration because everyone understands what their responsibilities are, and their  incentives are clear and tightly defined.

This approach had a particular appeal to governments, as they attempted to step back from the minutiae of delivering public services.  British Conservative governments used the approach to break up monolithic nationalised industries into individual entities, such as power generators and distributors.

They put this approach into effect at the top level of the railway system by splitting the task of running the track and the signalling (Railtrack’s job) from the role of operating the trains.  It is not surprising that Railtrack, born into this environment, carried the approach to its logical conclusion in its internal operation.

.  .  .

In 1937, the Nobel prize-winning economist Ronald Coase had explained that companies perform internally those tasks for which the transactional costs of outsourcing are too high.

What fuelled the outsourcing boom of the 1990s was the second unexamined assumption – that the cost of negotiating, monitoring and maintaining contractual relationships with outsourcing partners had dropped sharply, thanks to the revolution in electronic communications.  The management of a much bigger web of contractors – indeed, the creation of a “virtual company” – became feasible.

In practice, of course, the real costs of establishing and maintaining contracts are not those of information exchange but of establishing trust, alignment of interests and a common purpose.  Speedy and cheap electronic communications have only a minor role to play in this process, as Coase himself pointed out in 1997.

.   .   .

And perhaps that is the most useful lesson from the Railtrack story: it is essential to decide what tasks are vital to your corporate purpose and to devote serious resources to achieving them.   Maintaining thousands of miles of steel tracks and stone chippings may be a dull, 19th century kind of task.   But as Railtrack found, if you can’t keep the railway running safely, you haven’t got a business.”

Reference:

Peter Martin [2001]: Lessons from Railtrack.  The collapse has demolished some untested assumptions about outsourcing.  Financial Times, 2001-10-09, page 21.

Claude E. Shannon [1948/1963]: The mathematical theory of communication. Bell System Technical Journal, October and November 1948.  Reprinted in:  C. E. Shannon and W. Weaver [1963]: The Mathematical Theory of Communication. pp. 29-125. Chicago, IL, USA: University of Illinois Press.

Technorati Tags: Information Society

A recent issue of ACM Transactions on Computer Systems (ACM TOCS) carries a paper with a wonderful example of this principle.  Faced with a denial-of-service attack, they propose that a server ask all its clients to increase their messages to the server.  Most likely, attackers among the clients are already transmitting at their local full capacity, and so are unable to do this, which means that messages from attackers will form a decreasing proportion of all messages received by the server.   The paper abstract is:

This article presents the design, implementation, analysis, and experimental evaluation of speak-up, a defense against application-level distributed denial-of-service (DDoS), in which attackers cripple a server by sending legitimate-looking requests that consume computational resources (e.g., CPU cycles, disk). With speak-up, a victimized server encourages all clients, resources permitting, to automatically send higher volumes of traffic. We suppose that attackers are already using most of their upload bandwidth so cannot react to the encouragement. Good clients, however, have spare upload bandwidth so can react to the encouragement with drastically higher volumes of traffic. The intended outcome of this traffic inflation is that the good clients crowd out the bad ones, thereby capturing a much larger fraction of the server’s resources than before. We experiment under various conditions and find that speak-up causes the server to spend resources on a group of clients in rough proportion to their aggregate upload bandwidths, which is the intended result.

Reference:

Michael Walfish, Mythili Vukurutu, Hari Balakrishnan, David Karger and Scott Shenker [2010]:  DDoS defense by offense.  ACM Transactions on Computer Systems, 28 (1), article 3.

• The discipline of Computer Science has usually developed first through practice, and only later through theory.
  

  The first calculating machine was invented by Leibniz in 1671, while the first mathematical theory of computing machines was that of Turing in 1937. The first widespread programmable device was Jacquard’s Loom, invented in 1801, while the first mathematical theory of programme languages did not appear until the 1960s. The Internet has been operating (first as Arpanet) since 1969, yet we still lack a formal theory of interaction. The first online friendships between people who never met were between telegraph operators, and later between telephonists, in the 19th century. The first e-commerce network was the Florists Telegraph Delivery Association, created in the USA in 1910.

• It is therefore a profound mis-understanding to consider Computer Science to be a branch of Pure Mathematics.

The scope (the content) of the discipline of Computer Science comprises the behaviors of human artefacts of a certain sort, along with some natural phenomena. Without the artefacts, we would likely not have the theory, or at least not yet (and perhaps not for a long time). Without the theory, we would not fully understand the performance of the artefacts, so both are needed. But the artefacts came first, and practice should dominate. If the theory dominates, our discipline will shrivel and die, becoming as dessicated and as useless as mathematical economics.

• Artificial Intelligence (AI) is the study of thinking about ways of knowing and ways of acting.
  

  This statement updates a statement of Seymour Papert (1988, p.3), who considered only ways of knowing.

  Seymour Papert [1988]: One AI or Many? Daedalus, 117 (1) (Winter 1988): 1-14.

• Not all ways of thinking are equally effective in all situations.
  

  In particular, some means of representing knowledge are more effective than other representations for some purposes. For instance, non-probabilistic formalisms for representing uncertainty, such as Dempster-Shafer Theory and Possibility Theory, are more effective than Probability Theory for domains where knowledge may be inconsistent or incomplete (ie, domains where the Law of the Excluded Middle cannot be presumed to hold, such as in medical diagnosis and in criminal forensics). The statement in the previous sentence remains true even though many such non-probabilistic formalisms can be shown to be equivalent to second- or higher-order nested probabilistic formalisms. This equivalence is a quaint mathematical result; non-probabilisitic formalisms are often easier for ordinary humans to understand than nested probabilistic formalisms. This is why the statement in the second sentence in this paragraph is an instance of the statement in the first sentence.

• Corollary: It behooves no one in AI to be dogmatic about ways of thinking.
  

  This is one reason why I am not a Bayesian.

• Deductive reasoning over an abstract mathematical model will only provide information about the real world to the extent that the relationship between the model and reality is continuously dependent on the initial assumptions.
  

  In other words, the fact that the assumptions of a model are close to some real phenomenon tells us nothing about whether the outputs of the model are close to those of the real phenomenon if the relationship between model and reality is not continuous. If you derive some result from an assumption that participants in some interaction have infinite processing capabilities, for example, then it does not necessarily follow that the same or close result holds if their real processing capabilities are finite, even if very large. Economics has always suffered from forgetting this truth, but most mainstream economists only seem to have realized it following the Great Global Economic Crisis of 2007. Indeed, some of the so-called freshwater economists have still not realized it, alleging that their models are still good predictors.

• In domains with intelligent participants (such as economics and computer science), models may be performative.
  

  In other words, participants may decide their modes of behaviour based on what modelers have suggested, so that modeling becomes, in effect, a form of self-fulfilling prophecy.

  In Economics, for example, the Black-Scholes model of options pricing allowed traders to price options rigorously. To do so, traders adopted the assumptions made by the modelers (eg, that errors are normally distributed, that decision-makers maximize expected utility, etc).

  Philip Mirowski [2002]: Machine Dreams: Economics Becomes a Cyborg Science. Cambridge, UK: Cambridge University Press.

  For doctrines of nuclear warfare, decision-makers adopted the modes of analysis, assumptions, and decision-options suggested to them by game theorists. Some of these assumptions were questionable during the Cold War – for example, that all participants know and agree on the game they are playing. As a consequence, the US Government appears to have embarked in the late 1950s on a mission to ensure the leaders of the USSR were also using game theory (mainly by issuing high-level public statements asserting that game theory was of no use in military applications).

• We have entered an era when the prevailing paradigm for the notion of computation is computing-as-interaction.

This paradigm follows earlier paradigms of computation as calculation (c. 1600 – 1965), of computation as information-processing (1965 – 1980), and computation as cognition (1980 – 1995). The new paradigm changes everything. In particular, an abstract model of computers based on movie projectors (ie, Turing Machines) is woefully inadequate for computing where outputs may be needed before all the inputs arrive, where there are multiple threads of control, where programs may be created, composed with one another, and compiled when invoked (ie, at run-time), and where computational devices and software exist together in ever-on, dynamic ecologies. We await an adequate formal, mathematical account of computing-as-interaction, and the game semantics of Samson Abramsky et al, and the bigraphs of Robin Milner are possible candidates.

M. Luck, P. McBurney, S. Willmott and O. Shehory [2005]: The AgentLink III Agent Technology Roadmap. AgentLink III, the European Co-ordination Action for Agent-Based Computing, Southampton, UK.

• As a consequence, Computer Science has a lot to learn from disciplines that have studied interaction.
  

  Disciplines such as Argumentation Theory, the Philosophy of Language, Linguistics, Economics, Social Psychology, Sociology, Anthropology, Political Science, Marketing, Epidemiology, Ecology, and Biology.Hopefully, the learning will be in both directions. For example, it is possible (although, in my personal opinion, unwise and immoral) for economists to assume that all economic actors always act in their own self-interest, maximizing their perceived expected utility. No rational computer scientist could make this assumption, however (except pro tem), since we all know the prevalence of buggy code: such code means that software entities may act against their own-self interest, or the interests of their principals. Creating a computational theory of interacting economic actors which does not make such false and unfalsifiable assumptions will surely benefit Economics, as well as Computer Science.

  The two-way interplay of Computer Science with these other disciplines of interaction provides further evidence that Computer Science is not a branch of Mathematics.

• Conflict and disagreement is inevitable in open computer systems.
  

  It therefore seems absurd to use formal models in which conflict is not permitted. Instead, it behooves us to consider computational models which enable conflict to be identified, managed, mitigated, and possibly resolved. Argumentation theory, not classical logic, is appropriate here.

• The Killer App for multi-agent systems is Distributed Computing.
  

  I have lost count of the number of times I have been asked by people outside the agents community, particularly other computer scientists, to name the killer app for agent technologies. Look about! It is all around you!Agent methodologies and technologies enable us to model and simulate complex adaptive systems, such as distributed computer systems. They also enable us to engineer (to specify, to design, and to create) such systems. And they enable us to study the properties of such systems, and hence to manage and control them.

• Agents are not objects.
  

  Objects always execute when invoked, and execute as expected. Agents may not. Objects maintain persistent relationships between one another. Agent relationships may be dynamic.

The death has just occurred of Robin Milner (1934-2010), one of the founders of theoretical computer science.   Milner was an ACM Turing Award winner and his main contributions were a formal theory of concurrent communicating processes and, more recently, a category-theoretic account of hyperlinks and embeddings, his so-called theory of bigraphs.   As we move into an era where the dominant metaphor for computation is computing-as-interaction, the idea of concurrency has become increasingly important; however, understanding, modeling and managing it have proven to be among the most difficult conceptual problems in modern computer science.  Alan Turing gave the world a simple mathematical model of computation as the sequential writing or erasing of characters on a linear tape under a read/write head, like a single strip of movie film passing back and forth through a projector.  Despite the prevalence of the Internet and of ambient, ever-on, and ubiquitous computing, we still await a similar mathematical model of interaction and interacting processes.  Milner’s work is a major contribution to developing such a model. 

Robin was an incredibly warm, generous and unprepossessing man.   About seven years ago, without knowing him at all, I wrote to him inviting him to give an academic seminar; even though famous and retired, he responded positively, and was soon giving a very entertaining talk on bigraphs (a representation of which is on the blackboard behind him in the photo).  He joined us for drinks in the pub afterwards, buying his round like everyone else, and chatting amicably with all, talking both about the war in Iraq and the problems of mathematical models based on pre-categories.  He always responded immediately to any of my occasional emails subsequently.

The London Times has an obituary here, from which the photo is borrowed.

References:

Robin Milner [1989]: Communication and Concurrency. Prentice Hall.

Robin Milner [1999]: Communicating and Mobile Systems: the Pi-Calculus. Cambridge University Press.

Robin Milner [2009]: The Space and Motion of Communicating Agents. Cambridge University Press.

Technorati Tags: communicating processes, concurrency, interaction, interacting processes, bigraphs, Robin Milner

From the perspective of the national economy and technological progress, these proposals are extremely misguided, and should be opposed by us all.    They demonstrate a profound ignorance of where important ideas come from, of when and where and how they are applied, and of where they end up.  In particular, they demonstrate great ignorance of the multi-disciplinary nature of most socio-economically-impactful research.

One example will demonstrate this vividly.  As more human activities move online, more tasks can be automated or semi-automated.    To enable this, autonomous computers and other machines need to be able to communicate with one using shared languages and protocols, and thus much research effort in Computer Science and Artificial Intelligence these last three decades has focused on designing languages and protocols for computer-to-computer communications.  These protocols are used in various computer systems already and are likely to be used in future-generation mobile communications and e-commerce systems. 

Despite its deep technological nature, research in this area draws fundamentally on past research and ideas from the Humanities, including: 

• Speech Act Theory in the Philosophy of Language (ideas due originally to Adolf Reinach 1913, John Austin 1955, John Searle 1969 and Jurgen Habermas 1981, among others)
• Formal Logic (George Boole 1854, Clarence Lewis 1910, Ludwig Wittgenstein 1922, Alfred Tarski 1933, Saul Kripke 1959, Jaakko Hintikka 1962, etc), and
• Argumentation Theory (Aristotle c. 350 BC, Stephen Toulmin 1958, Charles Hamblin 1970, etc). 

Assessment of the impacts of research over five years is laughable when Aristotle’s work on rhetoric has taken 2300 years to find technological application.   Even Boole’s algebra took 84 years from its creation to its application in the design of electronic circuits (by Claude Shannon in 1938).  None of the humanities scholars responsible were doing their research to promote technologies for computer interaction or to support e-commerce, and most would not have even understood what these terms mean.  Of the people I have listed, only John Searle (who contributed to the theory of AI), and Charles Hamblin (who created one of the first computer languages, GEORGE, and who made major contributions to the architecture of early computers, including invention of the memory stack), had any direct connection to computing.   Only Hamblin was afforded an obituary by a computer journal (Allen 1985).

None of the applications of these ideas to computer science were predicted, or even predictable.  If we do not fund pure research across all academic disciplines without regard to its potential socio-economic impacts, we risk destroying the very source of the ideas upon which our modern society and our technological progress depend. 

Reference:

M. W. Allen [1985]: “Charles Hamblin (1922-1985)”. The Australian Computer Journal, 17(4): 194-195.

Technorati Tags: RAE

For instance, under the direction of John Fox, the Advanced Computation Laboratory at Europe’s largest medical research charity, Cancer Research UK (formerly, the Imperial Cancer Research Fund) applied Toulmin’s model of argument in computer systems they built and deployed in the 1990s to handle conflicting arguments in some domain.  An example was a system for advising medical practitioners with the arguments for and against prescribing a particular drug to a patient with a particular medical history and disease presentation.  One company commercializing these ideas in medicine is Infermed.    Other applications include the automated prediction of chemical properties such as toxicity (see for example, the work of Lhasa Ltd), and dynamic optimization of extraction processes in mining.

For me, Toulmin’s most influential work was was his book Cosmopolis, which identified and deconstructed the main biases evident in contemporary western culture since the work of Descartes:

• A bias for the written over the oral
• A bias for the universal over the particular
• A bias for the general over the local
• A bias for the timeless over the timely.

Formal logic as a theory of human reasoning can be seen as example of these biases at work. In contrast, argumentation theory attempts to reclaim the theory of reasoning from formal logic with an approach able to deal with conflicts and gaps, and with special cases, and less subject to such biases.    Norm’s dispute with Larry Teabag is a recent example of resistance to the puritanical, Descartian desire to impose abstract formalisms onto practical reasoning quite contrary to local and particular sense.

References:

S. E. Toulmin [1958]:  The Uses of Argument.  Cambridge, UK: Cambridge University Press. 

S. E. Toulmin [1990]: Cosmopolis:  The Hidden Agenda of Modernity.  Chicago, IL, USA: University of Chicago Press.

Technorati Tags: Stephen Toulmin, argumentation, logic

With the growth of the Internet and the Web over the last two decades, we have reached a position where a new metaphor for computation is required:  computation as interaction, or the joint manipulation of ideas and actions. In this metaphor, computation is something which happens by and through the communications which computational entities have with one another.  Cognition and intelligent behaviour is not something which a computer does on its own, or not merely that, but is something which arises through its interactions with other intelligent computers to which is connected.  The network is the computer, in SUN’s famous phrase.  This viewpoint is a radical reconceptualization of the notion of computation.

In this new metaphor, computation is an activity which is inherently social, rather than solitary, and this view leads to a new ways of conceiving, designing, developing and managing computational systems.  One example of the influence of this viewpoint, is the model of software as a service, for example in Service Oriented Architectures.  In this model, applications are no longer “compiled together” in order to function on one machine (single user applications), or distributed applications managed by a single organisation (such as most of today’s Intranet applications), but instead are societies of components:

• These components are viewed as providing services to one another rather than being compiled together.  They may not all have been designed together or even by the same software development team; they may be created, operate and de-commissioned according to different timescales; they may enter and leave different societies at different times and for different reasons; and they may form coalitions or virtual organizations with one another to achieve particular temporary objectives.  Examples are automated procurement systems comprising all the companies connected along a supply chain, or service creation and service delivery platforms for dynamic provision of value-added telecommunications services.
• The components and their services may be owned and managed by different organisations, and thus have access to different information sources, have different objectives, have conflicting preferences, and be subject to different policies or regulations regarding information collection, storage and dissemination.  Health care management systems spanning multiple hospitals or automated resource allocation systems, such as Grid systems, are examples here.
• The components are not necessarily activated by human users but may also carry out actions in an automated and co-ordinated manner when certain conditions hold true.  These pre-conditions may themselves be distributed across components, so that action by one component requires prior co-ordination and agreement with other components.  Simple multi-party database commit protocols are examples of this, but significantly more complex co-ordination and negotiation protocols have been studied and deployed, for example in utility computing systems and in ad hoc wireless networks.
• Intelligent, automated components may even undertake self-assembly of software and systems, to enable adaptation or response to changing external or internal circumstances.  An example is the creation of on-the-fly coalitions in automated supply-chain systems in order to exploit dynamic commercial opportunities.  Such systems resemble those of the natural world and human societies much more than they do the example arithmetical calculations  programs typically taught in Fortran classes, and so ideas from biology, ecology, statistical physics, sociology, and economics play an increasingly important role in computer science.

How should we exploit this new metaphor of computation as a social activity, as interaction between intelligent and independent entities, adapting and co-evolving with one another?  The answer, many people believe, lies with agent technologies.  An agent is a computer programme capable of flexible and autonomous action in a dynamic environment, usually an environment containing other agents.  In this abstraction, we have software entities called agents, encapsulated, autonomous and intelligent, and we have demarcated the society in which they operate, a multi-agent system.  Agent-based computing concerns the theoretical and practical working through of the details of this simple two-level abstraction.

Reference:

Text edited slightly from the Executive Summary of:

M. Luck, P. McBurney, S. Willmott and O. Shehory [2005]: The AgentLink III Agent Technology Roadmap. AgentLink III, the European Co-ordination Action for Agent-Based Computing, Southampton, UK.

Technorati Tags: computation, artificial intelligence, computation as interaction, software as a service, Service Oriented Architectures

Since the rise of file sharing, particularly illegal file sharing, over a decade ago, it has also been common to hear talk about Peer-to-Peer (P2P) architectures.   Conceptually, in these architectures all machines are viewed equally, and none are especially distinguished as servers.   Here, there is no central library of books; rather, each reader him or herself owns some books and is willing to lend them to any other reader as and when needed.   Originally, peer-to-peer architectures were invented to circumvent laws on copyright, but they turn out (as do most technical innovations) to have other, more legal, uses – such as the distributed storage and sharing of electronic documents in large organizations (eg, xray images in networks of medical clinics).

Both client-server and P2P architectures involve attempts at remote control.  A client or a peer-machine makes a request of another machine (a server or another peer, respectively), to undertake some action(s) at the location of the second machine.   The second machine receiving the request from the first may or may not execute the request.   This has led me to think about models of such action-at-a-distance.

Imagine we have two agents (human or software), named A and B, at different locations, and a resource, named X, at the same location as B.   For example, X could be an electron microscope, B the local technician at site of the microscope, and  A a remote user of the microscope. Suppose further that agent B can take actions directly to control resource X.   Agent A may or may not have permissions or powers to act on X.

Then,  we have the following five possible situations:

1.  Agent A controls X directly, without agent B’s involvement (ie, A has remote access to and remote control over resource X).

2.  Agent A commands agent B to control X (ie, A and B have a master-slave relationship; some client-server relationships would fall into this category).

3.  Agent A requests agent B to control X (ie, both A and B are autonomous agents; P2P would be in this category, as well as many client-server interactions).

4.  Both agent A and agent B need to take actions jointly to control X (eg, the double-key system for launch of nuclear missiles in most nuclear-armed forces; coalitions of agents would be in this category)

5.  Agent A has no powers, not direct nor indirect, to control resource X.

As far as I can tell, these five situations exhaust the possible relationships betwen agents A and B acting on resource X, at least for those cases where potential actions on X are initated by agent A.  From this outline, we can see the relevance of much that is now being studied in computer science:

• Action co-ordination (Cases 1-5)
• Command dialogs (Case 2)
• Persuasion dialogs (Case 3)
• Negotiation dialogs (dialogs to divide a scarce resource) (Case 4)
• Deliberation dialogs (dialogs over what actions to take) (Cases 1-4)
• Coalitions (Case  4).

To the best of my knowledge, there is as yet no formal theory which encompasses these five cases.   (I welcome any suggestions or comments to the contrary.)  Such a formal theory is needed as we move beyond Web 2.0 (the web as means to create and sustain social networks) to reification of the idea of computing-as-interaction (the web as a means to co-ordinate joint actions).

Reference:

Network Working Group [1999]: Hypertext Transfer Protocol – HTTP/1.1. Technical Report RFC 2616.  Internet Engineering Task Force.

Technorati Tags: client-server, peer-to-peer, remote control, action-at-a-distance, Web 2.0, computing-as-interaction