Intelligent
Architectures
for Electronic
Commerce
Part 1.5: Symbolic Reasoning
Agents
Agent Architectures (1)
• We want to build agents that are
autonomous, can react to appropriate stimuli,
act in a goal-directed manner, and interact
with other agents.
• The organisation of the
– knowledge representation,
– decision-making machinery, and
– agent/environment interface
is the architecture of a specific agent design.
Intelligent Architectures for Electronic Commerce
Timothy J Norman and Wamberto Vasconcelos
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Agent Architectures (2)
• Originally (1956-1985), most agents designed
within AI were symbolic reasoning agents.
Agents use explicit logical reasoning in order
to decide what to do (GOFAI).
• Problems with this approach led to the
reactive agents (BBAI) movement (1985present).
• From 1990-present, a number of hybrid
alternatives have been proposed.
Intelligent Architectures for Electronic Commerce
Timothy J Norman and Wamberto Vasconcelos
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Symbolic Reasoning Agents
(1)
• The classical approach to building agents is to
view them as a particular type of KBS.
• This is known as symbolic AI.
• A deliberative agent, or agent architecture, is
one that:
– contains an explicitly represented, symbolic model
of the world; and
– makes decisions (for example about what actions
to perform) via symbolic reasoning.
Intelligent Architectures for Electronic Commerce
Timothy J Norman and Wamberto Vasconcelos
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Symbolic Reasoning Agents
(2)
• If we are to build agents in this way, there are
two key problems to be solved:
– The transduction problem:
how to translate sensory data into an accurate,
adequate representation in time for it to be useful.
… vision, speech understanding, learning.
– The representation/reasoning problem:
how to symbolically represent complex real-world
entities and processes, and how to reason with this
information in time for the results to be useful.
… knowledge representation, automated reasoning.
Intelligent Architectures for Electronic Commerce
Timothy J Norman and Wamberto Vasconcelos
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Symbolic Reasoning Agents
(3)
• Most researchers accept that neither problem
is anywhere near solved.
• The underlying problem is the complexity of
symbol manipulation algorithms in general:
many (most) search-based symbol
manipulation algorithms of interest are highly
intractable.
Intelligent Architectures for Electronic Commerce
Timothy J Norman and Wamberto Vasconcelos
6
Decision Making as
Theorem Proving
• How can an agent decide what to do using
theorem proving?
• The basic idea is to encode a theory stating the
best action to perform in any situation.
• Let:
–  be this theory (typically a set of rules);
–  be a logical database that describes the current
state of the world;
– Ac be the set of actions the agent can perform;
–  ⊢  means that  can be proved from  using
Intelligent Architectures for Electronic Commerce
Timothy J Norman and Wamberto Vasconcelos

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Finding Actions
for each   Ac do
if  ⊢ Do () then
return 
end-if
end-for
for each   Ac do
if  ⊬ Do () then
return 
end-if
end-for
// find an action
// explicitly
// prescribed
// find an action
// not excluded
Intelligent Architectures for Electronic Commerce
Timothy J Norman and Wamberto Vasconcelos
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Example: Toy World
• Agent’s goal is to collect all the toys.
(0,2)
(1,2)
(2,2)
(0,1)
(1,1)
(2,1)
(0,0)
(1,0)
(2,0)
Intelligent Architectures for Electronic Commerce
Timothy J Norman and Wamberto Vasconcelos
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Domain Description
• We use three domain predicates for this
world:
– In (x,y )
– Toy (x,y )
– Facing (d )
agent is at location (x,y ).
there is a toy at location (x,y ).
the agent is facing direction d.
• Possible actions:
– Ac = {turn, forward, pickup }
(Note, turn means “turn right”.)
Intelligent Architectures for Electronic Commerce
Timothy J Norman and Wamberto Vasconcelos
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Decision Making Rules
• The rules  for determining what to do:
In (0,0)  Facing (north )  Toy (0,0)  Do (forward)
In (0,1)  Facing (north )  Toy (0,1)  Do (forward)
In (0,2)  Facing (north )  Toy (0,2)  Do (turn)
In (0,2)  Facing (east )  Do (forward)
In (x,y)  Toy (x,y)  Do (pickup)
• … and so on!
• Using these rules (and other obvious ones), starting
at (0,0) the agent will collect all the toys.
Intelligent Architectures for Electronic Commerce
Timothy J Norman and Wamberto Vasconcelos
11
Limitations
• There are a number of problems including:
– how to convert video camera input to Toy (x,y )
– decision making assumes the environment is static.
– decision making using first-order logic is undecidable!
(Even if propositional logic is used, in the worst case
we must solve co-NP-complete problems.)
• Typical solutions:
– weaken the logic;
– use symbolic, non-logical representations;
– shift emphasis of reasoning from run time to design
time.
Intelligent Architectures for Electronic Commerce
Timothy J Norman and Wamberto Vasconcelos
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AOP
• Much of the interest in agents from the AI
community stems from Shoham’s notion of
agent oriented programming (AOP).
• AOP was proposed as a `new programming
paradigm, based on a societal view of
computation’.
• The key idea is to directly program agents in
terms of intentional notions: belief,
commitment, etc.
Intelligent Architectures for Electronic Commerce
Timothy J Norman and Wamberto Vasconcelos
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AGENT0 (1)
• Shoham suggested that a complete AOP
system will have three components:
– a logic for specifying agents and describing their
mental states;
– an interpreted language for programming agents;
and
– an ‘agentification’ process for converting ‘neutral
applications’ (e.g. databases) into agents.
• Shoham proposed AGENT0 as an initial
proposal for providing the first two
components.
Intelligent Architectures for Electronic Commerce
Timothy J Norman and Wamberto Vasconcelos
14
AGENT0 (2)
• AGENT0 is an extension to LISP.
• Each agent in AGENT0 has four components:
– a set of capabilities (things that the agent can do);
– a set of initial beliefs;
– a set of initial commitments (things that the agent
will attempt to do — motivational states); and
– a set of commitment rules.
• The key component, which determines how
the agent acts, is the commitment rule set.
Intelligent Architectures for Electronic Commerce
Timothy J Norman and Wamberto Vasconcelos
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AGENT0 (3)
• Each commitment rule contains:
– a message condition;
– a mental condition; and
– an action.
• On each ‘agent cycle’:
– the message condition is matched against the
messages the agent has received; and
– the mental condition is matched against the
beliefs of the agent.
– If the rule fires, the agent becomes committed to
the action.
Intelligent Architectures for Electronic Commerce
Timothy J Norman and Wamberto Vasconcelos
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AGENT0 (4)
• Actions may be:
– private (an internally executed computation) or
– communicative (sending a message).
• Messages are constrained to be one of three
types:
– “requests” to commit to an action;
– “unrequests” to refrain from actions; and
– “informs” which are used to pass information.
Intelligent Architectures for Electronic Commerce
Timothy J Norman and Wamberto Vasconcelos
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The AGENT0 Architecture
Intelligent Architectures for Electronic Commerce
Timothy J Norman and Wamberto Vasconcelos
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Commitment Rule Example
COMMIT(
;; Message condition: I have received a REQUEST
;; from ‘agent’ to do ‘act’ at ‘time’.
(agent, REQUEST, DO(time, act)),
;; Mental condition: I believe that ‘agent’ is my
;; friend, I am capable of ‘act’ and I have no
;; other commitments at ‘time’.
(B, [now, Friend agent] AND
CAN(self, action) AND
NOT [time, CMT(self, anyact)]),
;; Then commit to doing ‘act’ at ‘time’.
self,
DO(time, action) )
Intelligent Architectures for Electronic Commerce
Timothy J Norman and Wamberto Vasconcelos
19
AOP Summary
• AOP is probably the first attempt at producing
an agent architecture where the emphasis is
on a ‘societal view of computation’.
• It was designed only as a prototype, and can
be seen as a development from the numerous
reactive planning agent architectures
proposed in the mid 1980s.
• However, reactive planning agent
architectures give us a more detailed picture
of practical reasoning.
Intelligent Architectures for Electronic Commerce
Timothy J Norman and Wamberto Vasconcelos
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