A HIGH-SPEED ARCHITECTURE FOR BUILDING HYBRID MINDS

Ois´ın Mac Fheara´ı, Mark Humphrys and Ray Walshe

School of Computing, Dublin City University, Dublin, Ireland

Keywords:

Collective intelligence, Agents, Distributed problem solving.

Abstract:

A resurgence of interest has taken place supporting the idea of an intelligence composed of many simple

components or “subminds”. There is a growing consensus that, rather than a small number of “elegant”

techniques for reasoning, inference or learning being the key to A.I., a model more likely to succeed might

consist of perhaps thousands of simple agents co-operating such that an emergent intelligence is seen. The

World-Wide Mind project is our attempt to facilitate this approach to scaling up artiﬁcial intelligence by

enabling large hybrid systems to be built by multiple authors. The goal of the research described in this paper

is to improve greatly the speed of the World-Wide Mind platform with a new communications protocol and

implementation, to improve the user API and human interfaces, and to investigate methods of automatically

constructing effective hybrid minds from the work of multiple authors, as well as to encourage collaboration

between A.I. researchers worldwide.

1 BACKGROUND

A resurgence of interest has taken place supporting

the idea of an intelligence composed of many simple

components or “subminds” (Minsky, 1985; Bryson,

2007; Brooks, 1991). Classical approaches to solv-

ing the A.I. problem might be criticised for betting

on single techniques to solve everything (e.g. neu-

ral networks, propositional logic, genetic algorithms,

planners), when it seems that humans rely on a large,

messy array of strategies and heuristics for cognitive

function (Minsky, 1985). There is a growing consen-

sus that, rather than a small number of “elegant” tech-

niques for reasoning, inference or learning being the

key to A.I., a model more likely to succeed might con-

sist of many simple agents co-operating such that an

emergent intelligence is seen.

1.1 The World-Wide Mind

We deﬁne a world as an instance of some problem

- such as a chess game or a block-stacking puzzle -

which may be solved by carrying out actions - such as

placing a chess piece on the board, or lifting a block.

At each timestep, the world produces a partial state

representing the agent’s sensory inputs. In a chess

game, this state will be the complete state: the posi-

tions of all the remaining pieces on the board. In other

worlds, the state will be a small subset of the complete

state - for example, in a poker game, the contents of

the opponents’ hands may not be visible. This state is

passed as input to a mind, which performs some com-

putation and returns a suggested action to perform in

the world. The pattern of interactions repeats until the

problem is solved or the run is otherwise terminated

(for example, a chess game ending in a tie).

This project attempts to make minds and worlds

available as services distributed across the internet

and encourage participation in the creation and use

of these services, and to facilitate the construction

of hybrid minds (Mind-Ms) which query subminds

for suggested actions. Previous efforts in enabling

large-scale multi-author collaboration have not con-

tinued into widespread use in building complex hy-

brid minds, which is partly what this work intends to

address.

2 RELATED WORK

There is a large body of work on the topic of modular

agent architectures, such as the Society of Mind (Min-

sky, 1985), the subsumption architecture (Brooks,

1991) and CogAff (Sloman, 2002). There are also a

number of implementations of distributed multi-agent

systems that fulﬁll different requirements (e.g. en-

cryption and fault tolerance in Cougaar (Helsinger

et al., 2004) and agent mobility in aglets), but the ex-

tensive functionality and ﬂexibility of these systems

659

Mac Fhearaí O., Humphrys M. and Walshe R..

A HIGH-SPEED ARCHITECTURE FOR BUILDING HYBRID MINDS.

DOI: 10.5220/0003186906590663

In Proceedings of the 3rd International Conference on Agents and Artiﬁcial Intelligence (ICAART-2011), pages 659-663

ISBN: 978-989-8425-40-9

 2011 SCITEPRESS (Science and Technology Publications, Lda.)

comes at a cost of some inaccessability for interested

newcomers. The aim of these frameworks is to fa-

cilitate multi-agent applications such as logistics and

online trading (Rogers et al., 2007), rather than ex-

plicitly to explore large-scale distributed cognition in-

volving multiple authors. These frameworks provide

architectures that make assumptions about how in-

formation should be represented and communicated,

how systems should be organised and what their re-

sponsibilities are. It is our belief that fewer require-

ments and responsibilities placed upon mind authors

may facilitate more participation.

A major goal of the W2M project is to minimise

the barrier to entry for researchers, students and ca-

sual programmers to create and test their minds and

worlds freely (Walshe et al., 2004). Authors are free

to deﬁne the world state and action representations, as

well as the interactions between sub-minds as they see

ﬁt. In our implementation we have selected a simple

scheme deﬁned by an XML-based markup language

where minds return a suggested action in response to

a getaction message containing the current state ob-

servable by the agent in the world, and worlds respond

to getstate and takeaction methods.

It is noted in (Bryson, 2007) that none of the lead-

ing architectures documented in an earlier review of

action selection work were still actively used seven

years later. Perhaps this suggests that, rather than

re-inventing architectures in an attempt to create in-

telligence, what might really be needed is simply a

large set of agents implementing various behaviours

and problem-solving strategies so that, while no one

person need understand every element of the whole

mind, it is collectively created by a large community.

Collaboration and sharing exists within artiﬁcial

intelligence research; for example, there are websites

which serve as repositories for machine learning code

(Kantrowitz, 2009) and training datasets (Asuncion

and Newman, 2009). These repositories are useful,

but the steps required to install or adapt an existing

solution differ in each instance and there is little con-

sistency in the types of programs and interfaces pro-

vided. Users must download the code and often patch

it to compile on their own machine, and must adapt

the program to suit the interface and problem structure

they wish to solve, if indeed the program addresses

the chosen problem.

RoboCup (Visser and Burkhard, 2007) and the

DARPA Challenge (Rouff, 2007) are closer to what

we wish to achieve, but the problem domains are spe-

ciﬁc and there is no clear way to build and share hy-

brid minds. These projects focus on competition but

not explicitly on re-use. Nevertheless the signiﬁcant

interest in the development of agents indicates that

collaborative approaches to A.I. research can be suc-

cessful.

There are limits on what one researcher or even

one lab can do. If we succeed in building a fast, us-

able platform which can be used to build diverseprob-

lem worlds and create minds composed of a multitude

of subminds - if multi-author collaboration could be

made easier - then the interest generated and subse-

quent studies could have a positive impact on A.I. re-

search.

3 WORLD-WIDE MIND “V2.0”

We outline here some of the recent changes that may

realise the vision of the World-Wide Mind project and

make it usable across the world.

3.1 Speed

The initial design of the platform (Humphrys, 2001)

embodied minds and worlds as web services, operat-

ing over the internet by sending messages over the

HTTP protocol. This enabled hybrid minds to be

built and evaluated (Walshe et al., 2004), but was im-

practically slow for large hybrids. When the system

was used as the basis for undergraduate assignments

in third year A.I. courses, the majority of submitted

minds were monolithic programs which did not seek

the advice of other minds to select actions.

As the primary objective of the project is for the

platform to be used widely by a large number of au-

thors, improving the latency and throughput of mes-

sages was a major concern.

3.1.1 Fast Communication Protocol

The implementation of minds and worlds as web

services afforded simplicity and transparency in dis-

tributing minds and worlds online. However, there is

a time penalty to be paid for the use of a web applica-

tion server (Apache Tomcat) and the HTTP protocol

to wrap messages.

To avoid these bottlenecks, the web application

server was replaced by a custom TCP-based server

which reads and writes XML messages across the net-

work as length-preﬁxed strings. Similarly, while one

goal of the platform is to be tolerant of errors in the

generated XML, we have optimised the case where

the XML is well-formed (since it will normally be

machine-generated).

ICAART 2011 - 3rd International Conference on Agents and Artificial Intelligence

660

3.1.2 Taking Advantage of Centralisation

Although videos were hosted online before Youtube

appeared, the act of hosting one’s videos was not a

simple task. There were issues with codecs, browser

plugin versioning and so on. The arrival of Youtube

caused an explosion in the amount of content placed

online by users - in part because it removed these

burdens of hosting and interoperability, but also be-

cause it served as a centralised, searchable directory

of videos accessible from any machine with internet

access.

We decided to host most of the minds and worlds

ourselves - that is, to set up a “Youtube of A.I.” to

which people may upload minds and worlds, so that

much re-use of other people’s work would involve di-

rect method calls, avoiding network access or parsing

of XML. The resulting increase in speed is signiﬁcant,

especially when a mind queries other minds to assist

in action-selection - since the queries are executed

serially, passing messages through the TCP stack as

XML would add a tangible latency to the process and

penalise the use of many subminds.

To support this, a new type of message was de-

vised: the continuerun message, when received by a

mind service, causes it to carry out the run directly

by taking actions in the world (to which the world re-

sponds by returning the newly-observed state, avoid-

ing the need to send explicit getstate messages). As

the mind carries out the run, it passes information on

states seen and corresponding actions taken back to

the client asynchronously. The client can see what

happened during a run without slowing down signiﬁ-

cantly the communication between world and mind.

Note that while these measures are intended to

greatly optimise speed in many cases, it may still be

necessary to rely on world and mind services on the

different servers. In this case, network and message

encoding/decoding latencies add an unavoidable time

penalty for each exchange of messages which is no-

ticeable on long runs.

As a result, the speed at which a conversation

is carried out between a mind or world service on

different servers has increased by a factor of 100

(with roundtrip time from ≈1s down to 10ms) al-

though this depends greatly on network latency and

mind/world complexity. In cases when the mind

and world are computationally simple and hosted on

the same server, we have seen performance improve-

ments of over a thousandfold.

3.2 User API

In the interests of ease-of-use and attractiveness to

newcomers, an API was developed for interacting

with the components of the system in a uniform man-

ner, without needing to deal with the XML or network

API. Interactions with remote minds or worlds are

encapsulated by the

RemoteMind

and

RemoteWorld

classes.

3.3 Web Interface

The initial implementation required the installation of

a Java program locally before users could perform and

view runs. To lower the entry barrier for users, an

AJAX-based front-end which relies on an XML back-

end was created. The web interface allows anybody to

start and examine runs, and to view a dynamic score-

board for each world. Users can also register on the

site and upload their own minds and worlds as JAR

ﬁles, making them immediately ready to run (and re-

use).

3.4 Visualisation of Runs

A graphical viewer was created by Ciarn O’Leary

for his implementation of Tyrrell’s simulated ani-

mal world (Walshe et al., 2004), displaying at each

timestep the two-dimensional grid with icons display-

ing locations of animals and other features in the

world. This viewer was extended into a uniform user

interface for world designers to render the world state

visually. Functionality was added by Brian Monks to

render runs as a series of images and as video.

3.5 Avoiding Inﬁnite Loops

Since we can neither guarantee the correctness of

user-supplied code nor reliably detect inﬁnite loops,

deadlocks and other difﬁcult errors, a heuristic

scheme was implemented which kills running ser-

vices that take too long to respond to a request. This

can and almost certainly will result in the termination

of runs which were slow for genuine reasons (such

as complex processing in a mind or world, or a mind

which queries a large number of remote subminds),

so the timeout threshold will be calibrated as more

minds and worlds become available.

3.6 Testing the System

The resulting system was opened up for undergrad-

uate computer science students tasked with writing

Mind-Ms to control an animal in a modiﬁed Tyrrell

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661

world (Tyrrell, 1993). The requirement for students

to write Mind-Ms resulted in a variety of different ap-

proaches, with some delegating to do-nothing minds

which return a constant value in certain situations

(e.g. “delegate to the Sleeper mind if we want to

sleep”), while some called successful minds on the

scoreboard (many of which were themselves Mind-

Ms) and some used more complex strategies to select

actions and minds.

3.7 Call Graph

It can be informative to see the set of subminds called

by an individual Mind-M, and in turn which minds are

called by them. This is addressed by the addition of a

call graph for each mind representing the set of minds

it calls during each run. The server tracks messages to

other minds and updates the call graph in a database.

4 RESULTS

An important question behind this work is whether a

single, open platform can encourage productive col-

laboration on artiﬁcial intelligence work by multiple

authors. To address this, we were able to use some

of the minds created for the modiﬁed Tyrrell world

by undergraduate students. Three non-hybrid minds

were selected from a total of 117 for use in a hybrid

mind, based on their names and the descriptions writ-

ten by each author, with the intention of satisfying

three goals: mating, sleeping in the animal’s den and

consuming food and water. Each of these subminds

scored poorly when tested individually, with the best

of the three (the mating mind) mating 11 times and

living for 218 timesteps.

A hybrid was created which uses simple

condition-rules to select which submind to obey

at each timestep - e.g.:

if (mateNearby) return

mater.getaction(state)

. This short program out-

performed each of the subminds, mating 44 times in

3626 timesteps on its best run. We believe the hy-

brid was successful because the high-levelbehaviours

speciﬁed by each mind collectively encapsulate a

good strategy for mating and survival. The hybrid was

able to select between these behaviours without ex-

plicitly encoding the low-level actions needed to sat-

isfy each goal.

5 FUTURE WORK

5.1 Inviting Collaboration

The platform itself will be of no use without problem

worlds and minds to solve the problems. As the capa-

bilities of the system grow, we will attempt to make

the system available to a wider spectrum of users; and

especially to artiﬁcial intelligence lecture courses and

researchers worldwide. It is our hope that the plat-

form will encourage users to collaborate by upload-

ing minds to work together on problems such that an

emergent intelligence is seen. Thus we hope to dis-

cover some novel methods of computation and digital

cognition via combinations of minds using this sys-

tem. Perhaps the most intelligent minds will make

use of other minds which the Mind-M author does not

(and need not) understand.

5.2 Communication between Subminds

Although hand-coded minds usually do not give out-

put other than a suggested action at each timestep,

many A.I. algorithms do produce some form of data

which could be used by hybrid minds, for example:

discounted reward in reinforcement learning (Sut-

ton and Barto, 1998), probabilities and degrees of

truth/belief in Bayesian analysis and fuzzy logic, or

heuristic solution cost in A*. This information could

be passed back and forth between the hybrid mind and

its subminds and used to reason about which mind

should be obeyed at a given moment, a task which is

currently difﬁcult (especially with subminds written

by third parties) due to the lack of a unifying platform

and API.

5.3 Constructing Hybrid Minds

So far we have not addressed the automatic construc-

tion of hybrid minds. To do so, we will carry out ex-

periments to create hybrids based on statistical analy-

sis of existing minds, ranking them by their perfor-

mance at various goals which together constitute a

holistic set of behaviours.

If we can break the problem into a series of needs

to be satisﬁed, and if we can measure how well each

need is satisﬁed by examining the states seen and ac-

tions taken during the run, then we can evaluate all

available minds on how well they satisfy each cho-

sen goal. For example, for the goal of minimising

thirst in the Tyrrell world, we can evaluate a par-

ticular mind by recording the average value of the

perceivedWaterShortage

sensory input. We can

then rank minds by their ability to satisfy the goal

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662

based on this metric. Once the minds which best sat-

isfy each of the goals selected are identiﬁed, then im-

plementing a hybrid mind could be a matter of speci-

fying rules for the conditions under which each mind

is called. Hybrid minds created with these techniques

will be tested against each other (and against hand-

built hybrids created by others) on the scoreboard.

6 CONCLUSIONS

There is currently no viable alternative platform

which enables the easy construction of hybrids of

other people’s work on a global scale. We hope to

make the platform so easy to use that it will attract

authors of many diverse worlds and minds to deposit

and test their programs on our site. Even if the project

fails to ﬁnd a signiﬁcant number of users, it will shed

light on future efforts and help point the way for fur-

ther research in this ﬁeld.

ACKNOWLEDGEMENTS

This research was funded by IRCSET’s Embark Ini-

tiative.

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