Deductive Reasoning Agents-Modern Computer Multiagent Systems-Lecture Slides, Slides of Multiagent Systems

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LECTURE 3:

DEDUCTIVE REASONING AGENTS

AGENT ARCHITECTURES An agent is a computer system capable of flexible autonomous action… Issues one needs to address in order to build agent-based systems… Three types of agent architecture :  symbolic/logical  reactive  hybrid

AGENT ARCHITECTURES Kaelbling considers an agent architecture to be: ‗[A] specific collection of software (or hardware) modules, typically designated by boxes with arrows indicating the data and control flow among the modules. A more abstract view of an architecture is as a general methodology for designing particular modular decompositions for particular tasks.‘

AGENT ARCHITECTURES Originally (1956-1985), pretty much all agents designed within AI were symbolic reasoning agents Its purest expression proposes that agents use explicit logical reasoning in order to decide what to do Problems with symbolic reasoning led to a reaction against this — the so-called reactive agents movement, 1985– present From 1990-present, a number of alternatives proposed: hybrid architectures, which attempt to combine the best of reasoning and reactive architectures

SYMBOLIC REASONING AGENTS If we aim to build an agent in this way, there are two key problems to be solved:

1. The transduction problem : that of translating the real world into an accurate, adequate symbolic description, in time for that description to be useful…vision, speech understanding, learning 2. The representation/reasoning problem : that of how to symbolically represent information about complex real-world entities and processes, and how to get agents to reason with this information in time for the results to be useful…knowledge representation, automated reasoning, automatic planning

SYMBOLIC REASONING AGENTS Most researchers accept that neither problem is anywhere near solved Underlying problem lies with the complexity of symbol manipulation algorithms in general: many (most) search-based symbol manipulation algorithms of interest are highly intractable Because of these problems, some researchers have looked to alternative techniques for building agents; we look at these later

DEDUCTIVE REASONING AGENTS /* try to find an action explicitly prescribed */ for each a  Ac do

if  |

Do ( a ) then return a end-if end-for /* try to find an action not excluded */ for each a  Ac do

if  |

 Do ( a ) then return a end-if end-for return null /* no action found */ 3-

DEDUCTIVE REASONING AGENTS An example: The Vacuum World Goal is for the robot to clear up all dirt 3-

DEDUCTIVE REASONING AGENTS Rules  for determining what to do: …and so on! Using these rules (+ other obvious ones), starting at (0, 0) the robot will clear up dirt 3-

DEDUCTIVE REASONING AGENTS Problems:  How to convert video camera input to Dirt( 0, 1 )?  decision making assumes a static environment: calculative rationality  decision making using first-order logic is undecidable! Even where we use propositional logic, decision making in the worst case means solving co-NP-complete problems (PS: co-NP-complete = bad news!) Typical solutions:  weaken the logic  use symbolic, non-logical representations  shift the emphasis of reasoning from run time to design time We will look at some examples of these approaches 3-

PLANNING SYSTEMS (IN GENERAL) Planning systems find a sequence of actions that transforms an initial state into a goal state 3- I G a a a

PLANNING Planning involves issues of both Search and Knowledge Rrepresentation Sample planning systems:  Robot Planning (STRIPS)  Planning of biological experiments (MOLGEN)  Planning of speech acts For purposes of exposition, we use a simple domain – The Blocks World 3-

THE BLOCKS WORLD

We also use predicates to describe the world:  ON(A,B)  ONTABLE(B)  ONTABLE(C)  CLEAR(A)  CLEAR(C)  ARMEMPTY 3-

A

B C

In general: ON(a,b) HOLDING(a) ONTABLE(a) ARMEMPTY CLEAR(a)

LOGICAL FORMULAS TO DESCRIBE FACTS ALWAYS TRUE OF THE WORLD And of course we can write general logical truths relating the predicates: [ $ x HOLDING(x) ]  ¬ ARMEMPTY " x [ ONTABLE(x)  ¬ $ y [ON(x,y)] ] " x [ ¬ $ y [ON(y, x)]  CLEAR(x) ] 3-

So…how do we use theorem-proving

techniques to construct plans?