Title: Diagnosing a team of agents: Scaling up - PowerPoint PPT Presentation

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Title: Diagnosing a team of agents: Scaling up

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Title: Diagnosing a team of agents: Scaling up Written by: Meir Kalech and Gal A. Kaminka Presented by: Reymes Madrazo-Rivera Goal: To explain a way of dealing with ... – PowerPoint PPT presentation

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Title: Title: Diagnosing a team of agents: Scaling up


1
Title Diagnosing a team of agents Scaling up
  • Written by Meir Kalech and Gal A. Kaminka
  • Presented by Reymes Madrazo-Rivera

2
Goal
  • To explain a way of dealing with disagreements
    when they occurs
  • in a team environment. The authors came up with
    three different
  • ways of detecting and diagnosing disagreements as
    soon as they
  • happen in team of behavior-based agents. They
    were especially
  • focused in reducing the communications and
    computational
  • requirements of the diagnosis for teams composed
    of
  • behavior-based agents.

3
Structure
  • Some useful concepts
  • Introduction to the topic
  • Previous work in this area
  • New methods for diagnosing disagreements
  • Experiments and conclusions

4
Useful concepts
  • Beliefs They provide a set of data describing
    the state of the environment at a given moment.
  • Behavior-based agents System which are
    decomposed into different processes or behaviors,
    where each process is responsible of interacting
    with a given characteristic of the environment
    1.
  • Behavior It is a mechanism of controlling the
    actions executed by the agent once certain
    preconditions, represented by predicates, are
    fulfilled. That is, given certain precondition
    that behavior can be executed. When analyzing
    behavior-based agents, for each agent is defined
    a hierarchy of behaviors, which are arranged from
    a general one to specific behaviors

5
Useful concepts (cont.)
  • Behavior path Specific sequence of behaviors
    followed given that for each one of them their
    preconditions were fulfilled. For example, for
    Fig.1 could be defined paths (A B E), A C F) and
    (A D E).
  • Team behavior Specific behavior path that should
    be executed at the same time for all members of
    team. For example, in Robocup could be defined
    two team behaviors, Attack and Defend, so that,
    given an initial precondition (s), all members of
    the team know that they have to follow certain
    behaviors to score a goal.

6
Introduction
  • When a team is playing, it should be an agreement
    between all the teammates
  • about what strategy in general to follow. That
    strategy is given by the team
  • behavior. Nevertheless it could be disagreements
    between members of a team
  • what is modeled by the fact that they execute
    different team behaviors. For
  • example, members of a team in Robocop are in
    Attack mode, but some of them
  • executed the behavior path corresponding to
    Defend mode for some reason.
  • The process followed to identify disagreements
    consists of comparing the agents
  • team behaviors and later identifying which
    beliefs led to that team behavior. This is
  • performed by a dedicated agent, who perform a
    diagnosis task of identifying the
  • team members beliefs and selecting the
    conflicting ones that led to the
  • disagreement.

7
Previous work for diagnosing disagreements
  • Two methods were used before this article
  • Reporting Each teammate communicate its belief
    to the diagnosing agent, and the last one
    compares each other to find contradictions. It
    requires polynomial times in the number of
    agents.
  • Querying It is longer process and it is the one
    that served as the base for the three new methods
    presented in this article. It has three steps
  • First, all those behaviors associated to the
    computed actions (linear complexity in the number
    of behaviors) are searched by creating later a
    set of behavior-path hypotheses that contains
    behaviors associated to the observed actions.
  • For each one of those behavior-path hypotheses,
    the diagnosing agent creates a set of belief
    hypotheses that are related to that specific
    behavior path hypothesis. Belief hypotheses are
    all the possible values to evaluated
    preconditions. For example, for a precondition of
    (p ? q), the possible hypotheses are (p ? q), (p
    ? ?q) and (?p ? q). (exponential complexity in
    the number of beliefs)
  • With those beliefs hypotheses, the diagnosing
    agent can start querying the observed agent to
    determine if it is traversed the same behavior
    path that matches with the computed actions.
  • At the end the observer agent has the beliefs for
    each observed agent and compares them (polynomial
    complexity).

8
New methods for diagnosing disagreements
  • Three methods were proposed in this article. Each
    one to reduce the complexities
  • mentioned before
  • Behavior querying This one avoids the long
    process of deducing all possible path behaviors
    by using communication. That is, by querying the
    analyzed agent about the path that it followed.
    In that way are avoided the rest of possible
    behavior path that were not used. So now, what it
    was a process linear complexity process in the
    number of behaviors becomes in a constant
    complexity problem O(1). In fact, also will be
    reduced the number of belief hypotheses to deal
    with, because it will be carried out just for one
    behavior path instead of a group of them.

9
New methods for diagnosing disagreements (cont.)
  1. Shared belief This one looks for reducing the
    exponential growth in the number of beliefs that
    is associated with a given behavior path. The
    idea now is to infer the prepositions associated
    to a belief, without considering their values.
    So, once the diagnosing agent has the
    prepositions associated to each agents
    preconditions, it compares those prepositions for
    each pair of analyzed agent to find share
    prepositions, which actually could be the source
    of contradictions. For example, consider that
    agents A and B consider prepositions (p,q) and
    (p,r) respectively. So, to determine whether A
    and B disagree, it will be enough to querying A
    and B for both values of the share preposition p
    (p and ?p). It could be that A believes p and B
    believes ?p, and that is the source of
    disagreement that caused that each one execute a
    different behavior path (Those values could be
    sensed differently because agents were in
    different physical locations). So far
    communications between agents keep increasing
    with the number of agents because all pair of
    agents are compared. But, the complexity reduce
    considerably (to a linear process in the number
    of beliefs) since inferring the exponential
    number of believe hypotheses is not necessary
    anymore.

10
New methods for diagnosing disagreements (cont.)
  1. Grouping The last process carry out by the
    diagnosing agent is the comparison between
    teammates beliefs. This is a polynomial process
    in the number of agents to compare and in the
    number of beliefs involved. This last method
    creates groups of agents composed of those with
    similar role into the team and the same behavior
    path. As a consequence, the diagnosing agent will
    select a single agent from each group to compare
    their beliefs, instead of comparing the belief of
    each observed agent. This method first uses
    behavior querying to get each agent behavior
    path. Agents groups are then built by considering
    that previous information and each observed
    agents role. Then the diagnosing agent select
    one observed agent per group and carry out for
    them a share believe process to finally determine
    possible sources of disagreements between them.

11
Experiment design and valuation
  • Those methods were evaluated on a multi-agent
    system (ModSAF). This
  • application models teams of helicopter pilots.
    For that experiment were considered
  • two roles for pilots and four behavior paths. The
    number of agents was increased
  • from 6 to 150 with an step of eight.
  • Four models were considered for the study in
    addition to the initial methods of
  • Reporting and Querying
  • Behavior The diagnosing agent just uses behavior
    querying (so, it works with the proper behavior
    path). So, it completes the process by generating
    belief hypotheses and querying observed agents
    about those belief hypotheses.
  • Belief The share belief method is used to
    generate belief hypotheses (so, it works with
    values of preconditions prepositions shared by
    agents).
  • BehaviorBelief Its a combination of the two
    first. After determining the behavior path of a
    given agent by using behavior querying, then
    possible disagreement is identified by using
    share belief.
  • Grouping It is also a combination of the two
    first method, but this time groups of agents
    according to their role and behavior paths are
    created, and after that the share belief is
    applied to diagnose disagreements, but the
    comparison will be carried out just between the
    representative agents of each group.

12
Experiment design and valuation (cont)
  • The experiment consisted of plotting each model
    in two different graphs. One of
  • (Number of Beliefs Messages Used vs Number of
    Agents) and the other one of
  • (Runtime in Milliseconds vs Number of Agents).
  • The conclusion for that test was that the runtime
    grows polynomially in the number
  • of agents for all the models due to the number of
    comparisons, except for the
  • Grouping one, where the complexity is reduced to
    linear growth because we are
  • dealing with a fixed number of comparisons. That
    reduced number of comparisons
  • also leads to a reduction in the number of
    messages. The two other new method
  • by themselves (behavior querying and share
    belief) do not seem to have much
  • contribution in the reduction of runtime or the
    number of messages. That happened
  • because in that experiment the authors were
    focused in checking the influence of
  • the number of agents and that is why the number
    of beliefs were small as well as
  • the number of behaviors.

13
Conclusions
  • It was presented a method that reduces the
    communication and
  • run-time when diagnosing disagreements between
    teammates
  • when a great deal of agents is involved. Firstly
    was reduced the
  • number of communications initially by querying
    about the behavior
  • path followed. Later on the communication was
    much more
  • reduced by creating groups of agents according to
    their role and
  • their computed behavior path. Consequently, the
    diagnosis
  • process continues by considering a representative
    agent of each
  • group.

14
THANKS
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