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Autonomous Agents . Overview . Topics. Theories: logic based formalisms for the explanation, analysis, or specification of autonomous agents. Languages: agent-based programming languages.

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topics
Topics
  • Theories: logic based formalisms for the explanation, analysis, or specification of autonomous agents.
  • Languages: agent-based programming languages.
  • Architectures: integration of different components into a coherent control framework for an individual agent.
topics3
Topics
  • Multi-agent architectures: methodologies and architectures for group of agents (could be from different architectures)
  • Agent modeling: modeling other agents’ behavior or mental state from the perspective of an individual agent
  • Agent capabilities
  • Agent testbeds and evaluation
agent theories languages and architectures

Agent Theories, Languages, and Architectures

Wooldridge & Jennings

(ATAL 1994, LNAI 890)

what is an agent
What is an agent?
  • Weak:
    • Autonomy
    • Social ability
    • Reactivity
    • Pro-activities
  • Strong:
    • Mental properties such as knowledge, belief, intention, obligation
    • Emotional
  • Others attributes: mobility, veracity, benevolence, rationality
agent theories
Agent Theories
  • How to conceptualize agents?
  • What properties should agents have?
  • How to formally represent and reason about agent properties?
agent theories7
Agent Theories
  • Definition: an agent theory is a specification for an agent.

 Formalisms for representing and reasoning about agent properties

  • Starting point: agent = entity ‘which appears to be the subject of beliefs, desires, etc.’
intentional system
Intentional system
  • An intentional system whose behavior can be predicted by the method of attributing belief, desires, and rational acumen
  • Proved that can be used to describe almost everything
  • Good as an abstract tool for describing, explaining, and predicting the behavior of complex systems
intentional system examples
Intentional system - Examples
  • One studies hard because one wants to get good GPA.
  • One takes the course ‘cs579-robotic’ because one believes that it will be fun.
  • One takes the course ‘cs579-robotic’ because there is no 500-level course offered.
  • One takes the course ‘cs579-robotic’ because one believes that the course is easy 
agent attitudes
Agent Attitudes
  • Information attitudes: related to the information that an agent has about the environment
    • Belief
    • Knowledge
  • Pro-attitudes: guide the agent’s actions
    • Desire
    • Intention
    • Obligation
    • Commitment
    • Choice
  • An agent should be represented in terms of at least one info-attitude and one pro-attitude. Why?
representing intentional notions
Representing intentional notions

Representing

Jan believes Cronos is the father of Zeus

naïve translation into FOL:

Believe(Jan, Father(Zeus,Cronos))

  • Problems:
    • No nested predicate
    • Zeus = Jupiter
    • Believe(Jan, Father(Jupiter,Cronos)) [Wrong]

Conclusion: FOL is not suitable since intention is context dependent.

possible world semantics
Possible World Semantics
  • Hintikka: 1962 – Agent’s belief can be characterized as a set of possible worlds.
  • Example:
    • A door opener robot: door is closed, lock needs to be unlocked but the robot does not know if the lock is unlocked or not – two possibilities:
      • {closed, locked}
      • {closed, unlocked}
    • Card player (poker): ?
    • UNIX Ping command: ?
possible world semantics13
Possible World Semantics
  • Each world represents a state that the agent believes it might be in given what it knows.
  • Each world is called a epistemic alternative.
  • The agent believes in something is true in all possible worlds.
  • Problem: logical omniscience – agent believes all the logical consequences of its belief  impossible to compute.
alternatives to pws
Alternatives to PWS
  • Levesque – belief and awareness: explicit belief (small) from implicit belief (large).
    • No nested belief
    • The notion of a situation is unclear
    • Under certain situation: unrealistic prediction
  • Konolige – the deduction model: modeling the belief of a symbolic AI system (database of beliefs and an inference system).
    • Simple
others
Others
  • Meta-language: one in which it is possible to represent the properties of another language
    • Problem: inconsistency
  • Pro-attitudes: goals and desires – adapting possible world semantics to model goals and desires
    • Problem: side effects
theory of agency
Theory of agency
  • Realistic agent:
    • combination of different components
    • dynamic aspect
  • Moore – knowledge and action: study the problem of knowledge precondition for actions
    • I needs to know the telephone number of my friend Enrico in order to call him.
    • I can find the telephone number in the telephone book.
    • I needs to know that the course is easy before I sign up for it 
theory of agency17
Theory of agency
  • Cohen and Levesque – belief and goal: originally developed as a pre-requisite for a theory of speech acts but proved very useful in analysis of conflict and cooperation in multi-agent diaglogue, cooperative problem solving
theory of agency18
Theory of agency
  • Rao and Georgeff – belief, desire, intention (BDI) architecture: logical framework for agent theory based on BDI, used a branching model of time
  • Singh: logics for representing intention, belief, knowledge, know-how, communication in a branching-time framework
theory of agency19
Theory of agency
  • Werner: general model of agency based on work in economics, game theory, situated automate, situated semantics, philosophy.
  • Wooldridge: modeling multi-agent system
agent architectures
Agent Architectures
  • Construction of computer systems with properties specified by an agent theory.
  • Three well-know architectures:
    • Deliberative
    • Reactive
    • Hybrid
deliberative architecture
Deliberative architecture
  • View agent as a particular type of knowledge based system – known as symbolic AI
  • Contains an explicit represented, symbolic model of the world
  • Decision is made via logical reasoning (pattern matching, symbolic manipulation)
  • Properties:
    • Attractive from the logical point of view
    • High computational complexity (FOL: not decidable, with modalities: highly undecidable)
slide22

Sense

  • Assimilate
  • Sensing results
  • Reasoning
  • Symbolic
  • representation
  • of the world
  • Determine what
  • to do next
  • Act
  • Execute the
  • action generated
  • by the reasoning
  • module

ENVIRONMENT

Deliberative architecture in picture

deliberative architecture23
Deliberative architecture
  • Examples:
    • Planning agents: a planner is an essential component of any artificial agent
      • Main problem: intractability – addressed by techniques such as hierarchical, non-linear planning.
    • IRMA (Intelligent Resource-bounded machine architecture): explicit representations of BDI & planning library, a reasoner, opportunity analyser, a filtering process, a deliberation process (mainly: reduced the time to deliberate)
deliberative architecture24
Deliberative architecture
  • HOMER: a prototype of an agent with linguistic capability, planning and acting capability.
  • GRATE*: layered architecture in which the behavior of an agent is guided by the mental attitudes of beliefs, desires, intentions, and joint intention.
reactive architecture
Reactive architecture
  • Proposed to overcome the weakness of symbolic AI
  • Main features:
    • does not include any kind of central symbolic world model
    • does not use complex reasoning
slide26

Sense

  • Assimilate
  • Sensing results
  • Reasoning
  • Determine what
  • to do next
  • Act
  • Execute the
  • action generated
  • by the reasoning
  • module

ENVIRONMENT

Reactive architecture in picture

reactive architecture27
Reactive architecture
  • Brook - behavior language: subsumption architecture
    • Hierarchy of task-accomplishing behaviors
    • Each behavior competes with others
    • Lower layer represents more primitive task and has precedence over upper layers
    • Very simple
    • Demonstrate that it can do a lot
    • Multiple subsumption agents
reactive architecture28
Reactive architecture
  • Arge and Chapman – PENGI: most everyday activity is ‘routine’
    • Once learned, a task becomes routine and can be executed with little or no modification
    • Routines can be compiled into a program and then updated from time to time (e.g. after new tasks are added)
reactive architecture29
Reactive architecture
  • Rosenschein and Kaelbling - Situated automata
    • Agent is specified in declarative terms which are then compiled into digital machine
    • Correctness of the machine can be proved
    • No symbol manipulation in situated automata, thus efficient
  • Maes – Agent network architecture: an agent is a network of competency modules
hybrid architecture
Hybrid architecture
  • Combine deliberative and reactive architecture – exploit the best out of the two
  • Georgeff and Lansky – Procedural Reasoning System: BDI & plan library, explicit symbolic representation of BDI
    • Beliefs are facts – FOL
    • Desires are represented by behavior
    • Each plan in the plan library is associated with invocation condition  reactive
    • Intention – the set of currently active plans
slide31

Environment

Plan

Library

Belief:

FOL

System

Interpreter

P1:

Invocation I1

Desire:

System beha.

Pn:

Invocation In

Intention:

Active

Pi:

Invocation Ii

Pj:

Invocation Ij

PRS in picture

hybrid architecture32
Hybrid architecture
  • Ferguson – TOURINGMACHINES:
    • Perception and action subsystem – interact directly with the environment
    • Control framework system: three control layers – each is independent, activity producing, concurrently executing process
      • Reactive layer (response to events that happen too quickly for other to response)
      • Planning layer (select plan, actions to achieve goal)
      • Modeling layer (symbolic representation, use to resolve goal conflict)
hybrid architecture33
Hybrid architecture
  • Burmeister et al. – COSY: hybrid BDI with features of PRS and IRMA, for a multi-agent testbed called DASEDIS
  • Mueller et at. – INTERRAP: layered architecture, each layer is divided into knowledge and control vertical part
agent language
Agent language
  • A system that allows one to program hardware and software computer systems in terms of some of the concepts developed by agent theorists.
  • Shoham – agent-oriented programming:
    • A logical system for defining the mental state of agents
    • An interpreted programming language for programming agents
    • An ‘agentification’ process, for compiling agent program into low-level executable systems

 Agent0: first two features

agent language35
Agent language
  • Thomas – PLACA (Planning communicating agent language)
  • Fisher – Concurrent METATEM: correctness of the agents with respect to their specification
  • IMAGINE project: ESPIRIT
  • General Magic, Inc. – TELESCRIPT
  • Connah and Wavish - ABLE