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LOGIC PROGRAMMING

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SCHOOL OF COMPUTER SCIENCE & INFORMATICS Multi-Agent Systems(MAS) & Distributed Artificial Intelligence(DAI) G.M.P. O Hare Lectures 5 & 6 Distributed Artificial ... – PowerPoint PPT presentation

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Title: LOGIC PROGRAMMING


1
University College Dublin SCHOOL OF
COMPUTER SCIENCE INFORMATICS Multi-Agent
Systems(MAS) Distributed Artificial
Intelligence(DAI) G.M.P. OHare Lectures 5 6
2
Distributed Artificial Intelligence
Distributed Artificial Intelligence(DAI)
-Endeavours to achieve Intelligent Systems not
by constructing a large Knowledge-Based System,
but rather by partitioning the knowledge domain
and developing 'Intelligent Agents',each
exhibiting expertise in a particular domain
fragment. This group of agents will thereafter
collectively work towards the solution of global
problems.
3
The Co-operating Experts Metaphor
This solution of problems by a group of agents,
providing mutual assistance as and when necessary
is often referred to as the..... "Community of
Co-operating Experts Metaphor" Smith and
Davis, Lenat, Hewitt Proponents of this
philosophy believe that reciprocal co-operation
is the cornerstone of society.
4
Social Agents
Domain Specific Knowledge Base
Q?
R AND P -gt Q
M 2.4
3
M?
P?
6
R 4 P 6
L 5 S 4
2
M -gt P
L OR S -gt M
S?
Aquuaintance Model
R?
L?
4
5
5
Why Distributed Artificial Intelligence?
  • Mirrors Human Cognition
  • Potential Performance Enhancements
  • Elegantly Reflects Society
  • Incremental Development
  • Increased Robustness
  • Reflects Trends in Computer Science in General
  • Strong Analogies to Decompositional Techniques
    employed
  • in Software Engineering

6
Coordination Paradigms
  • Numerous Different Paradigms have been
    proposed....
  • The Blackboard Model (Reddy et al)
  • The Actor Model (Hewitt)
  • The Contract Net Approach (Smith and Davis)
  • The 'BEING' Concept (Lenat)
  • Hybrid Approaches

7
The Blackboard Model
DAI first presented itself in the form of a
blackboard model in the HEARSAY I,II and III
(Carnegie Mellon Univ) speech understanding
systems The Blackboard model involves agents
communicating by way of a shared global data
structure called the 'Blackboard' Agents could
not communicate directly with each other but
only via the contents of the blackboard.
8
Poll-Hypothesis-Test
The Model generally involves a poll-hypothesis-tes
t cycle. In the case of the HEARSAY systems this
consisted of.... Initially a hypothesis is
installed on the Blackboard regarding the meaning
of a particular utterance poll - each ks is
polled in order to ascertain if it can refine
the. hypothesis Those
which can indicate their ability to do so
with an associated confidence
factor hypothesis - The ks boasting the
highest cf is invoked making the
appropriate refinement to the hypothesis
on the blackboard. test - The
other kss evaluate the amendment and based on
the collected response
the amendment is either adopted or
ignored The hypothesis is successively
refined repeating this cycle until the
utterance is identified with a sufficient degree
of confidence
9
Blackboard Problems
DAI approaches seem to have followed a course
similar to those developments which have taken
place in the design of real-time languages The
blackboard exhibits obvious similarities to
Hoare's Monitor concept and in general the
Mail-box concept It also suffers from the same
limitations - Congestion
Problems Reliability Problems
Thus there was a realisation as in real-time
language design that.... "To divorce data
transmission and process synchronisation was
totally unnatural"
Young, Real-Time Languages
10
The Actor Model
Hewitt's Actor Model was one such example of
later synchronised approaches The society of
co-operating experts were to be modelled by a
basic building block called an Actor Actors
could communicate with other Actors via
messages The behaviour of an actor was to be
defined dependent upon which message it received
and these potential actions are contained in a
Script Actors could communicate with those
actors with whom they were acquainted as
indicated by way of their acquaintance
list This model therefore offered point to
point communication
11
The Contract Net Protocol I
The Contract Net Approach regarded the
distribution of tasks amongst agents, or the
connection problem as a process of
negotiation. Contract Net Protocol- A
node(the manager) advertises a problem via a
broadcast to all other nodes(potential
contractors). Potential contractors compare this
and other problems and upon identification of
the tasks for which they are most suited they
submit bids. The manager evaluates bids and
awards contract accordingly
12
Contract Net II
The communication protocol is vastly superior in
that both manager and potential contractor
participate in the decision regarding a suitable
contract It is also worth noting that here
three addressing modes are offered general
broadcast, limited broadcast and point to
point. This approach adopts an inter agent
language which while simple is capable of
supporting the relevant communication.
13
Beings
Lenat commissioned a very different approach when
trying to overcome problems of inter agent
understanding. In his PUP6 system an agent was
represented by a 'Being' which had to comply
with a predefined structure. It consisted of a
fixed number of 'parts', each part representing a
question that the ks may be equipped to
answer. If a part contained a value then the
being is sufficiently knowledgeable with the
value representing a procedural attachment which
would yield the appropriate answer. When a
being asks a question it must therefore stipulate
the relevant part. This approach made question
answering relatively trivial - essentially
pattern matching. It avoided the need for an
inter node language, however in so doing it
enforced a very stylised form on each
agent Furthermore there was no provision for
gaining expertise.
14
Hybrid Approaches
Hybrid Approaches - Lesser and Corkill 1981 -
Huhns et al 1983
15
Benevolence Competition
Within all of these approaches there is this
underlying presumption that the intelligent
agents necessarily want to co-operate.
"The Benevolent Agent
Assumption" More recently a school of thought
believes that this is not necessarily the case
and agents may have conflicting goals This has
resulted in ....
"The Conflicting Agents Assumption"
Geneserth and Rosenchien Stanford HPP Project
16
Problems with DAI
Identification of appropriate task
decomposition and task distribution
strategies Optimisation of problem solution
(Cammarata et al 1982,1983) Difference
of opinion between experts where the mapping
between expertise and experts is not 1 1
but 1 n - need conflict resolution
strategies Problems with understanding
Handling uncertainty becomes even more
problematic Need deadlock avoidance
strategies Problems with heterogenous
nodes Interoperability
17
Reactive v Classical Systems
Essentially Multi-Agent systems occupy a point on
a continuum between two extreme classes of
system. These two extremes are...
The classical system
The reactive or
situated action system We propose a compromise
that of the
'Deliberate Social Agent'
18
Classical System
Contemplative
X
Deliberate Social System
Reasoning
X
Complexity
Reactive
Situated Action System
Reactive
X
Internal Model
Percieve the world
Represent the world
19
Reactive or Situated Systems
Agents react to varying situations and
consequently do not have an explicit
representation of the world within which they
exist. Reasoning takes place within each agent
at a very low level, essentially each agent has
little more than an ability to perform pattern
matching. A given situation is characterised
and matched against a collection of rules
specifying appropriate behaviour associated
with each of these situations ie situation
-action or situated action. Typically the
actions associated with a given situation are
often very simple and consequently the agents
themselves are very simple computational
entities. Even though each of the individual
agents are very simple the global complexity and
global structures can be achieved as a result of
the emergent property of the interacting
behaviours of the community of agents.
20
Reactive Systems Assessment
Advantages simplicity. avoidance of
necessity for a sophiciated representation of the
world and more significantly the problems
of maintaining this model. generally the
structure of agent interaction is well defined
and domain independent. Disadvantages
New sets of rules need to be designed for each
application. Each situation needs to be
specified and identified so as to have
an associated rule. Difficulty in solving
inherently recursive problems. Lack of a
precise theory upon which the combining
behaviours of agents can be based and explained.
21
Reflective Systems
Generally the agents within a reflective system
are more complex computational entities. They
do not merely react to a given situation in a
specific way. In fact they may often react in
different ways dependent on their own beliefs
or intentions. Such systems necessitate an
internal representation of the world. They often
base their reasoning on the actions of the other
agents within the community. They normally
possess some model of intentionality which
represents their goals, desires, prejudices,
beliefs etc. about themselves and the remainder
of the community. Certain classes of problem
seem to necessitate this ability to reason using
intentionality. The wisest man puzzle seems to
typify these.
22
Reflective Systems II
  • Reasoning intentionally normally demands use of
  • higher order logics.
  • Particularly Modal logics.
  • - epistemic logics
  • - doxastic logics
  • There are two general approaches
  • Sentential logics (Konolidge)
  • Possible World Logics (Kripke)

23
Deliberative Systems Assessment
  • Advantages
  • A clear theoretical reasoning model that
    underpins
  • the approach
  • A mental state that is verifiable and traceable
  • Amenable to modeling using higher order logics
  • Disadvantages
  • Theoretical Model is complex and unwieldy
  • Approach is more computationally demanding
  • Less appropriate for time critical reasoning
    scenarios
  • Necessitates the maintenance of a model of the
    environment

24
The Cognitive Chasm
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