Agent models.

1
Agent models.

• EEL 5937 Multi Agent Systems
• Lotzi Bölöni

2
Basic agent model

• An agent is a triplet of A ltAg, KB, Sgt
• Ag agenda. The things the agent wants to
  accomplish.
• Can be defined as a function of the knowledgebase
  Ag Ag(KB)
• Also known as goal
• KB knowledgebase. The sum of the knowledge of
  the agent about its environment and itself.
• Also known as state, model of the world
• S strategy.
• -Action S(KB, Ag)
• -Also known as behavior
• -The strategy of the agent generates actions
  based on the knowledgebase and the agenda.

3
Reactivity

• If a program environment is guaranteed to be
  fixed, the program can just execute blindly.
  E.g. compiler
• In the real world things change and the
  information is incomplete. The interesting
  environments are dynamic.
• Software is harder to build for dynamic
  environments.
• A reactive agent is one that maintains an ongoing
  interaction with its environment, and responds to
  changes that occur in it (in time for the
  response to be useful).

4
Pro-activeness

• Reacting to the environment is easy (e.g.
  stimulus-gt response rules)
• But we generally want agents to do things for us.
  
• Hence goal directed behavior.
• Pro-activeness generating and attempting to
  achieve goals not driven solely by events,
  taking the initiative.
• Recognizing opportunities.

5
Purely reactive agent

• Purely reactive agent
• No knowledgebase / No state
• The knowledge of the agent is restricted to the
  most recent sensor input
• Easily expressible as
• Action-reaction tables
• Rule-sets
• The behavior of the agent does not depend on its
  history
• E.g. thermostat

6
Agenda

• Alternative names tasks, desires etc.
• There are two common types
• Achievement agenda those of the form achieve
  state of affairs X. They are specified by a set
  G of good or goal states. The agent succeeds if
  brings about at least one of these states.
• Maintenance agenda those of the form maintain
  state of affairs X. They are specified by a set
  B of bad states. The agent succeeds if it manages
  to avoid all states in B.
• Typically a boolean function or a distance
  function on the knowledgebase.

7
Agent Building Problem

• We call the states of the world where the agenda
  is satisfied desired state of the world.
• Given a agenda Ag and an initial set of knowledge
  KB0 create a strategy S which works towards the
  agenda.
• In general, when designing agents
• The agenda is a given in the specification of the
  agent
• The initial knowledge is given (but the agent
  builder frequently needs to work to achieve a
  good knowledge representation)
• The strategy needs to be created by the agent
  builder
• The knowledge of the agent needs to be gathered
  by the agent builder.

8
Pitfall Self deceiving agents

• While we are talking about a desired state of the
  world, the agenda is defined on the
  knowledgebase, not on the world
• The easiest way for an agent to satisfy the
  agenda is to change only the knowledgebase. It is
  so much easier than bothering with the world!
• There is no automatic guarantee that the
  knowledgebase is accurate.
• Solution
• The agent needs to take explicit actions to
  update and refresh the knowledgebase.
• It is better to update the knowledgebase based on
  the feedback from the environment, instead of the
  predicted effect of the action.

9
Events and actions

• Events are a set of changes in the environment
• E lt?x1, ?x2 ?xn gt
• An action is an event where the cause of the
  event can be identified as the agent.
• The changes contained in an action are both in
  the environment and the agent (typically the
  agent knowledgebase).
• We assume that the events and actions are
  instantaneous. We assume that there is a strict
  ordering between the events and actions.

10
Perception / Sensing

• Perception is a special kind of action, performed
  by an agent to update its knowledgebase.
• A perception is valid, if after the perception,
  the knowledgebase is a better approximation of
  the world than before.
• A perception is pure if the changes contained by
  it apply exclusively to the knowledgebase.
• It is difficult to achieve a pure perception (see
  the Heisenberg uncertainty principle)
• But we can perform logical deductions on the
  knowledgebase, which improve our knowledge
  without modifying the environment.
• Some people might argue that this kind of
  reasoning do not increase the amount of
  knowledge.

11
Implementing strategies

• Strategies are the active component of any agent.
• It is very difficult to just sit down and
  implement a monolitic strategy which encapsulates
  the whole functionality of the agent.
• The first, pragmatic step is to decompose the
  strategies.
• At some point, we cannot decompose further, so we
  actually need to start implementing it
• Either by direct, imperative programming
• By employing logical models, such as BDI
• Using neural networks
• Using genetic algorithms