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Intelligent Architectures for Electronic Commerce Part 1.5: Symbolic Reasoning Agents.

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Presentation on theme: "Intelligent Architectures for Electronic Commerce Part 1.5: Symbolic Reasoning Agents."— Presentation transcript:

1 Intelligent Architectures for Electronic Commerce Part 1.5: Symbolic Reasoning Agents

2 Intelligent Architectures for Electronic Commerce Timothy J Norman and Wamberto Vasconcelos 2 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.

3 Intelligent Architectures for Electronic Commerce Timothy J Norman and Wamberto Vasconcelos 3 Agent Architectures (2) Originally ( ), 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 (1985- present). From 1990-present, a number of hybrid alternatives have been proposed.

4 Intelligent Architectures for Electronic Commerce Timothy J Norman and Wamberto Vasconcelos 4 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.

5 Intelligent Architectures for Electronic Commerce Timothy J Norman and Wamberto Vasconcelos 5 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.

6 Intelligent Architectures for Electronic Commerce Timothy J Norman and Wamberto Vasconcelos 6 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.

7 Intelligent Architectures for Electronic Commerce Timothy J Norman and Wamberto Vasconcelos 7 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 

8 Intelligent Architectures for Electronic Commerce Timothy J Norman and Wamberto Vasconcelos 8 Finding Actions for each   Ac do if  ⊢  Do (  ) then// find an action return  // explicitly end-if// prescribed end-for for each   Ac do if  ⊬   Do (  ) then// find an action return  // not excluded end-if end-for

9 Intelligent Architectures for Electronic Commerce Timothy J Norman and Wamberto Vasconcelos 9 Example: Toy World Agent’s goal is to collect all the toys. (0,0) (2,1)(1,1) (2,2)(1,2) (0,1) (0,2) (2,0)(1,0)

10 Intelligent Architectures for Electronic Commerce Timothy J Norman and Wamberto Vasconcelos 10 Domain Description We use three domain predicates for this world: –In (x,y ) agent is at location (x,y ). –Toy (x,y ) there is a toy at location (x,y ). –Facing (d ) the agent is facing direction d. Possible actions: – Ac = {turn, forward, pickup } (Note, turn means “turn right”.)

11 Intelligent Architectures for Electronic Commerce Timothy J Norman and Wamberto Vasconcelos 11 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.

12 Intelligent Architectures for Electronic Commerce Timothy J Norman and Wamberto Vasconcelos 12 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.

13 Intelligent Architectures for Electronic Commerce Timothy J Norman and Wamberto Vasconcelos 13 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.

14 Intelligent Architectures for Electronic Commerce Timothy J Norman and Wamberto Vasconcelos 14 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.

15 Intelligent Architectures for Electronic Commerce Timothy J Norman and Wamberto Vasconcelos 15 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.

16 Intelligent Architectures for Electronic Commerce Timothy J Norman and Wamberto Vasconcelos 16 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.

17 Intelligent Architectures for Electronic Commerce Timothy J Norman and Wamberto Vasconcelos 17 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.

18 Intelligent Architectures for Electronic Commerce Timothy J Norman and Wamberto Vasconcelos 18 The AGENT0 Architecture

19 Intelligent Architectures for Electronic Commerce Timothy J Norman and Wamberto Vasconcelos 19 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) )

20 Intelligent Architectures for Electronic Commerce Timothy J Norman and Wamberto Vasconcelos 20 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.


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