Social intelligence testbeds

tarix30.01.2018
ölçüsü445 b.

Social intelligence testbeds

  • Social intelligence testbeds

  • Social Properties

    • Interactivity, non-neutralism, competitive and cooperative anticipation

  • Instrumental Properties

    • Discrimination, grading, boundedness, team symmetry, reliability, efficiency

  • Univocal Properties

    • Validity

  • Examples of application to some environments

  • Conclusions



Issues about the evaluation of social intelligence

  • Issues about the evaluation of social intelligence

    • What makes a MAS social? The agents or the environment?
      • Some have studied this focussing on the agents:
        • Hibbard’s adversarial matching pennies (Hibbard 2008-2011).
        • Darwin-Wallace distribution (Hernandez-Orallo et al 2011).
    • What makes social and general intelligence different?
    • How can the influence of the other agents be regulated?


We first analyse multi-agent systems in terms of:

  • We first analyse multi-agent systems in terms of:

    • Usual MAS with actions, observations and rewards.
      • Simultaneous for every agent.
    • Agent slots and line-ups
      • The same environment can be instantiated with different sets of agents, leading to very different behaviours.
    • Teams
      • In practice, it is unlikely that alliances and coalitions appear spontaneously.
      • We consider the existence of previously defined teams
        • Rewards are the same for all members in a team.


We use a customary definition:

  • We use a customary definition:

    • Set of agents (e.g., robocup players)
    • Distribution of line-ups (e.g., Pr teammates and opponents)
    • Set of environments (e.g., several game configurations)
    • Distribution of environments (e.g., Pr configurations)
    • Distribution of slots (e.g., positions of the evaluated agent)


In a multi-agent environment:

  • In a multi-agent environment:

    • A rich configuration may lack any social interaction if other agents have no effect on the reward of the evaluated agent.
    • The ability of the opponents is key, especially for competitive social intelligence.
    • The ability of the teammates is also key, especially for cooperative social intelligence.
    • The way in which we sample the distributions is also important.


We have introduced a series of formal properties to analyse the suitability of a multi-agent environment to evaluate social intelligence:

    • We have introduced a series of formal properties to analyse the suitability of a multi-agent environment to evaluate social intelligence:


Interactivity (Action dependency):

  • Interactivity (Action dependency):

    • Action sensitivity to other agents.
      • Whether the inclusion of different agents in the multi-agent environment has an effect on what the evaluated agent does.
  • Non-neutralism (Reward / slot result dependency)

    • Effect of other agents on the evaluated agent’s rewards.
    • From the six forms of symbiosis in ecology:
      • Neutralism (0,0), amensalism (0,-), commensalism (+,0), competition (-,-), mutualism (+,+), and predation/parasitism (+,-).
        • We can simplify this to neutralism, cooperation (including commensalism and mutualism) and competition (including the rest).
        • Non-neutralism measure: 0 (neutralism) > 0 (cooperation), <0 (competition)


Competitive anticipation

  • Competitive anticipation

    • The evaluated agent can perform better if their opponents/competitors can be well anticipated.
      • It is measured relative to the results against random agents.
  • Cooperative anticipation

    • The evaluated agent can perform better if their teammates/cooperators can be well anticipated.
      • It is measured relative to the results with random agents.


Discrimination

  • Discrimination

    • Given a set of agents, we want the testbed to give significantly different values to the agents so that their social abilities can be discriminated.
  • Grading (strict total grading or partial grading)

    • Measures how much the metrics resemble a total order or, more precisely, how frequent is that for three agents (a,b,c) if a ≤ b, b ≤ c then a ≤ c, when placed in different slots.
      • This can be calculated for a strict total order or for a partial order


Boundedness

  • Boundedness

    • Weights for environments, agents and line-up being bounded (or being probability measures).
    • Zero-sum teams (in the limit). Given several teams, the sum of rewards of all teams sum up to 0.
  • Team symmetry

    • If we make the environment team-symmetric, in terms of positions inside the team (intra-team) and between teams (inter-team), we do not need the slot distribution.
      • Many games are not team-symmetric:
        • Prey-predator
        • Football (goalkeepers very different from other players)


Reliability:

  • Reliability:

    • How close the measured value is to the actual value given by the definition.
      • Tests sample over the distributions of environments, slots and agents, and have to limit trial duration.
  • Efficiency

    • How much reliability can be achieved in terms of the time devoted to testing.
      • It depends on how representative and effective the sampling over the distributions is.


Validity:

  • Validity:

    • Main testbed pitfalls may originate from two reasons.
      • If the testbed allows for good performance without social intelligence.
        • Social characteristics are not very relevant and general intelligence must suffice.
      • If social intelligent agents do not get good performance in the testbed.
        • The test may measure some other abilities that are not social intelligence.


We have applied the properties to several MAS:

  • We have applied the properties to several MAS:

    • Five MAS environments/games have been analysed:
      • Matching pennies (any slot)
      • Prisoner’s dilemma (any slot)
      • Predator-prey (3 predators, 1 prey, evaluee acts in predator slot)
      • Pac-man (any slot)
      • RoboCup Soccer (any slot)
    • Using

      all possible agents

      .


The ranges are wide if all possible agents are considered.

  • The ranges are wide if all possible agents are considered.

    • The analysis changes radically when using families of agents instead of all.


For the instrumental properties there is more diversity.

  • For the instrumental properties there is more diversity.

  • Validity problems originate because many other abilities are more relevant than social intelligence for these environments.

    • Also, the first two lack cooperation.
  • Reliability problems, as many environments are stochastic.

    • Even with same line-up and slots, results can be very different.
    • With several repetitions, the average can converge fast for some of them (efficiency).


We have derived a series of formal, effective properties to characterise multi-agent systems in terms of how necessary and sufficient social intelligent is for them.

  • We have derived a series of formal, effective properties to characterise multi-agent systems in terms of how necessary and sufficient social intelligent is for them.

  • The properties are more fine-grained and allow for a more informative characterisation of a testbed.

    • Go well (but controversially) beyond game theory equilibria and other properties.
  • Using five environments as examples, we have seen that the set of agents that is considered is crucial.

    • Considering all possible agents leads to virtually any possibility in any game.
  • Main questions for future work.

    • Define reasonable subsets of agents, using agent description languages and see how the ranges for the properties change for these subsets.
    • How many different games/environments are necessary so that the particularities of the games/environments are finally irrelevant for the aggregate measure?
    • Communication and language have been left out.

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