Perfect Disruption: The Paradigm Shift from Mental
Agents to ORGs
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Hewitt, C. “Perfect Disruption: The Paradigm Shift from Mental
Agents to ORGs.” Internet Computing, IEEE 13.1 (2009): 90-93.
© 2009 IEEE
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http://dx.doi.org/10.1109/MIC.2009.15
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E d i t o r : C h a r l e s Pe t r i e • p e t r i e @ s t a n f o r d .e d u
Perfect Disruption
The Paradigm Shift
from Mental Agents to ORGs
Carl Hewitt • MIT EECS (emeritus)
T
he mental agent paradigm has had some
success in modeling and simulating
­human-like behavior. However, computing has changed dramatically since its invention, and we’re now in the midst of a “perfect
disruption” brought on by changes to hardware,
software, and applications.
The hardware transformation is driven by
the many-core architecture, which will soon
support thousands of threads in processes for
widely used software applications using semantic integration (described later). On the
software front, look forward to changes from
client cloud computing,1,2 in which information
is permanently stored in servers on the Internet
and cached temporarily on clients that range
from single chip sensors, handhelds, notebooks,
desktops, and entertainment centers to huge
data centers. (Even data centers are clients that
often cache their information to guard against
geographical disaster.) Client cloud computing
will provide much-needed new capabilities such
as maintaining client privacy by storing information on servers encrypted so that it can be
decrypted using only the client’s private key;
providing greater integration of user information obtained from competing vendors’ servers
without requiring the vendors to interact with
each other; and providing better advertising
relevance and targeting without exposing client privacy. In terms of applications, scalable
semantic integration is poised to bring together
information from calendars and to-do lists;
email archives; physical, psychological, and
social presence information; documents of all
sorts; contacts (including social graphs); and
search results.
This column examines how this perfect disruption is causing a paradigm shift from mental
agents to organizations of restricted general90 Published by the IEEE Computer Society
ity (ORGs) as the foundation for implementing
large-scale Internet applications.
How ORGs Address
the Perfect Disruption
ORGs is a name for the paradigm in which
people are tightly integrated with information
technology that enables them to function organizationally.1–3 ORGs formalize existing practices to provide a framework for addressing issues
of authority, accountability, scalability, and robustness using methods that are analogous to
human organizations. (Of course, as any reader
of Dilbert comics knows, such organizational
practices are imperfect.) Functional specialization within ORGs aids scalability by dividing up
the workload and aids robustness by requiring
specialized suborganizations to negotiate agreements according to their differing viewpoints
and responsibilities.
ORGs are structured around organizational
commitments, defined as information pledged.3
For example, ORGs can use contracts to formalize their mutual commitments to fulfill specified
obligations to each other. Yet, manifestations of
information pledged will often be inconsistent.
For example, any given contract might be internally inconsistent, or two contracts in force at
one time could contradict each other. Issues that
arise from such inconsistencies can be negotiated among ORGs.
ORGs are very well suited to a many-core
architecture. They can be implemented by
­actors — the universal primitives of concurrent
computation.4,5 ORGs are message composable,
meaning that systems are composed by sending
messages. Contrast that with update composability, in which systems are composed by updating
a transactional memory.6,7 Message composability has numerous advantageous over update
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IEEE INTERNET COMPUTING
Perfect Disruption
composability, including modulariy,
scalability, and the fact that message
composability works across the entire computational spectrum — from
within many-core chips to geographically distributed systems — whereas
transactional memory works only
within the address space of a single
process on a single computer.
ORGs provide guidance on how to
extend the X86 architecture (AMD,
Intel, Via, and so on) to make it more
suitable for many-core to provide
• concurrent nonstop automatic
storage reclamation (garbage collection) and relocation to improve
efficiency;
• prevention of memory corruption that otherwise results from
programming languages such as
C and C++ using thousands of
threads in a process; and
• nonstop migration of ORGs (while
they’re in operation) within a
computer and between distributed computers.
ActorScript5 is a kind of actor
programming language that’s well
suited for implementing messagecomposable ORGs. Distributed ORG
implementations can accommodate
client cloud computing by maintaining some parts on the clients and
some on the servers. Scalable semantic integration is also well suited for
ORG-based implementation because
ORGs supply a framework for implementing two critical components:
• a scalable semantic engine provides
inference and action capabilities
for semantic information, and
• an intuitive semantic user interface based on ontologically
extended natural language provides visualization and interactive capabilities for semantic
information.
ORGs have important advantages o­ver
mental agents, as I describe later.
The Dream of Mental Agents
A mental agent is defined behaviorally as cognitively operating in humanlike fashion. The paradigm is deeply
and pervasively psychological.8,9 The
most popular kind of mental agent
can be characterized as BDIA: beliefs,
desires (goals), intentions (plans), and
affect (emotions). It has moved beyond its original sequential conceptualization by introducing parallelism,
which can be used for low-level I/O
(vision, for example), (associative)
memory operations, and other basic
operations as performed by the parts
of the brain. Yet, none of these changes the mental agent paradigm, which
draws its fundamental strength from
staying close to the mental operations
of a single person.
The development of mental
agents has continued steadily since
the earliest days of artificial intelligence, and researchers have realized impressive achievements (see
the Summer 2006 issue of AI Magazine for some examples). That said,
progress in using mental agents as a
foundation for software applications
has been frustratingly slow. Impressive demonstrations of mental
agents’ capabilities in some application areas have repeatedly failed
to garner widespread commercial
adoption. Nevertheless, researchers continue the quest to develop
mental agent frameworks for software systems. Expressions of confidence and hopes for the future have
long been regular features of conferences.10 Researchers have been
heartened because no convincing
principled arguments have proven
it impossible; indeed, human behavior presents a kind of existence
proof that something like the mental agent paradigm can be made to
work. Moreover, the community has
evolved and gained insights into
multiagent systems.
In contrast to the situation with
ORGs, the perfect disruption is causing mental agents to lose ground.
First, many-core architectures pose
a challenge because the computer’s
information processing is no longer
anything like a person’s information processing:
• Using human-like mental operations becomes an increasing
bottleneck as the number of cores
grows because the cores perform
independent tasks.
• Using a human-like I/O system
becomes a bottleneck as the number of interconnections increases
because the wires carry independent messages.
Scalable semantic integration requires capabilities beyond those of
a mental agent. The current family
of standards (such as OWL 211) represent significant progress. However, the semantics of OWL 2 need
improvement:
• OWL 2 semantics require that
ontologies (theories) be absolutely consistent. A single inconsistency can allow anything
and everything to be inferred
(for example, “the moon is
made of green cheese”), whereas
adopting paraconsistency would
make it possible to reason more
safely in the presence of inconsistent information.5
• OWL 2 semantics are for single
anonymous ontologies (theories).
In contrast, ORGs require the simultaneous use of multiple interrelated ontologies (theories).3
• OWL 2 semantics don’t support
reification reflection5 — the ability
to abstract propositions from text
and to reify propositions to text —
which is needed to relate natural
language I/O to reasoning.
• Furthermore, OWL 2 semantics
are rigid and inexpressive because
they don’t support ontologically
extended natural language.
Fortunately, these limitations to the
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W3C specifications can be overcome
in a way that substantially preserves
the value of work already done using the current W3C specifications
so that it doesn’t have to be completely redone.
The mental agent paradigm might
increasingly be used in avatars (both
human- and animal-like) and cognitive models12 of individual humans,
but operational implementations will
require ORGs, just as all large software systems will. The original conception of the development of mental
agents is thus turned upside down:
instead of ORGs being implemented
using mental agents, humans will be
and scalability. As Petrie predicted,15
the old agent technology has essentially disappeared from large-scale
software systems. Consequently, a
conundrum is emerging, such that
researchers must choose whether to
• stay the current mental agents
course despite the paradigm switch
from mental agents to ORGs, or
• change course and adopt the ORGs
paradigm as fundamental, thus
begging the question, “Where are
the agents?”
Many artificial intelligence researchers have long presupposed that
No software agent architectures can compete
with ORGs in understandability, manageability,
and scalability.
simulated and avatars will be implemented using ORGs!
The Software Agent
Conundrum
According to a published consensus
of researchers, a software agent is
basically a mental agent adapted for
software engineering.13 More general conceptions have been attempted
without success. As Charles Petrie noted, for example, “some have tried to
offer the general definition of agents
as someone or something that acts on
one’s behalf, but that seems to cover
all of computers and software.”14
Despite many years of trying,
none of the software agent development systems for large-scale Internet
applications have had any significant
commercial success. ORGs (although
not currently called such by practitioners) are trumping mental agents
for implementing large-scale Internet systems. No software agent architectures can compete with ORGs
in understandability, manageability,
92 agents are a principal subject of their
field. This is especially poignant for
the Autonomous Agents and Multiagent Systems (AAMAS) community,
which includes the term in its name
twice. AAMAS is a vibrant community whose members are performing
exciting and important research, but
its conceptual foundations are badly
in need of reformulation because
of the paradigm shift from mental
agents to ORGs.
A
s Microsoft’s Steve Ballmer suggested in a recent appearance at
the Churchill Club, this perfect disruption represents a “perfect opportunity” that’s enabling fundamental
new capabilities in the computer
industry. I believe his assessment is
correct. As a result, IT will be
• easier to use (leading to civilized
discourse in interactions with
people, unlike current systems,
which are often unresponsive
and provide no explanations of
what they’ve done and why),
• more useful (semantic integration of information from diverse
sources), and
• more privacy-friendly because
of
­client-based
information
integration.
Similarly, IT will become more
valuable as revenues increase and
new IT employment opportunities
arise for ontologists, ORG technologists, and discourse experts. In the
midst of the current downturn, we
can look forward to a more prosperous future.
Acknowledgments
Charles Petrie suggested the phrase “perfect
disruption” and made other valuable suggestions for improvement. Jean-Pierre Briot,
Jeremy Forth, Michael Genesereth, Fanya
S. Montalvo, and members of the Stanford
Logic Group provided helpful discussion
and comments.
References
1. C. Hewitt, “ORGs for Scalable, Robust,
Privacy-Friendly Client Cloud Computing,” IEEE Internet Computing, Sept./Oct.
2008, pp. 96–99.
2. C. Hewitt, “A Historical Perspective on
Developing Foundations for Client Cloud
Computing: The Paradigm Shift from
Inconsistency Denial to Rapid Recovery” (revised version of “Development of
Logic Programming: What Went Wrong,
What Was Done About it, and What It
Might Mean for the Future” AAAI Workshop on What Went Wrong, AAAI 08.),
Google Knol, 2008; http://perspective.
carlhewitt.info.
3. C. Hewitt, “Norms and Commitment for
ORGs (Organizations of Restricted Generality): Strong Paraconsistency and
Participatory Behavioral Model Checking,” Google Knol, 2008; http://normsand
commitmentfororgs.carlhewitt.info.
4. C. Hewitt, P. Bishop, and R. Steiger, “A
Universal Modular Actor Formalism for
Artificial Intelligence,” Proc. Int’l Joint
Conf. Artificial Intelligence (IJCAI 73),
www.computer.org/internet/
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Perfect Disruption
1973, pp. 235–245; http://dli.iiit.ac.in/
ijcai/IJCAI-73/PDF/027B.pdf.
5. C. Hewitt, “Common Sense for Concurrency and Strong Paraconsistency using
Unstratified Inference and Reflection,”
Google Knol, 2008; http://commonsense.
carlhewitt.info.
6. T. Knight, “An Architecture for Mostly
Functional Languages,” Proc. ACM Lisp
and Functional Programming Conf., ACM,
1986, pp. 105–112.
7. T. Harris et al., “Composable Memory
Transactions,” Comm. ACM, Aug. 2008,
pp. 91–100.
8. Y. Wang and J. Laird, Integrating Semantic Memory into a Cognitive Architecture,
tech. report CCA-TR-2006-02, Center for
Cognitive Architectures, Univ. of Michigan, 2006.
9. M. Minsky, The Emotion Machine, Simon
& Schuster, 2006.
10. S. Benfield, J. Hendrickson, and D. Galanti,
“Making a Strong Business Case for MultiAgent Technology,” Proc. Autonomous
Agents and Multi-Agent Systems (AAMAS
06), ACM Press, 2006, pp. 10–15.
11. B. Motik, P. Patel-Schneider, and B. Grau,
OWL 2 Web Ontology Language: Direct
Semantics, W3C recommendation, 8 Oct.
2008; www.w3.org/TR/2008/WD-owl2
-semantics-20081202/.
12. E. Norling and F. Ritter, “Towards Supporting Psychologically Plausible Variability in Agent-Based Human Modeling,”
Proc. Autonomous Agents and MultiAgent Systems (AAMAS 04), ACM Press,
2004, pp. 758–765.
13. M. Huhns et al., “Research Directions for
Service-Oriented Multi-Agent Systems,”
IEEE Internet Computing, Nov./Dec. 2005,
pp. 65–70.
14. C. Petrie, “Agent-Based Engineering,
the Web, and Intelligence,” IEEE Expert,
Nov./Dec. 1996, pp. 24–29.
15. C. Petrie, “Agent-Based Software Engineering,” invited talk, Practical Application of Intelligent Agents and Multiagent
Technology ( PAAM 00), 2000; http://
www-cdr.stanford.edu/~petrie/agents/
comm/.
Carl Hewitt is emeritus at Massachusetts
Institute of Technology. His research
interests include logic programming,
concurrency, strongly paraconsistent
logic, and the philosophy and sociology
of science and engineering. Hewitt has
a PhD in mathematics from MIT. He was
the IBM Chair Visiting Professor in the
Department of Computer Science at Keio
University in Japan from September 1989
to August 1990. Contact him via www.
carlhewitt.info.
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