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Multi-layered Multi-agent Situated System. M MA S S. Motivations about space. MAS models do not explicitly consider the spatial structure of agent environment despite of the fact that

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motivations about space
Motivations about space
  • MAS models
    • do not explicitly consider the spatial structure of agent environment
    • despite of the fact that
      • Recent results suggest that the topology of agent interaction is critical to the nature of the emergent behavior of the MAS
      • A large class of problems is characterized by unavoidable spatial features: several domains deal with physical space (e.g. localization problems) or a logical space (e.g. information flow in an organizational structure)

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an example mutual awareness in coordination
An example: mutual awareness in coordination
  • Coordination among people is performed through mutual perception, possibly mediated by artificial agents
  • Logical space (categories and their relations) provides a topology to compute mutual awareness
  • Mutual awareness depends on the logical distance among people

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m ma s s
MMASS

A multi-layered situated MAS: agent actions and interactions are strongly dependent on their position in the structured environment

  • Situated in an heterogeneous environment (multi-layered)
    • in its properties and/or in its structure
    • neighborhoods are not uniform across the space
  • Composed by heterogeneous agents
    • Different capabilities and behaviors for agents of different types
    • Different sensitivity to external stimuli
  • Heterogeneous interaction mechanisms
    • ‘Reaction’ among adjacently situated agents
    • ‘Field diffusion’ throughout the spatial structure of agent environment

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mmass ancestors
MMASS ancestors

Rooted on basic principles of

  • Cellular Automata
    • intrinsically include the notions of state and spatial structure ===> uniformity
    • Extension of CA-based models
  • GAMMA (=> Chemical abstract machine)
    • Chemical metaphor
    • Inclusion of space topology

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mmass and l mass
MMASS and L*MASS
  • Agent behavior 

perception-deliberation-action mechanism

    • Perception of local environment (e.g. adjacent sites, fields)
    • Action selection according to agent state, position and type
    • Action execution
  • Language for MASS (L*MASS)  Set of primitives to specify agent actions
    • intra-agent : trigger() and transport()
    • inter-agent : emit() and react()

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mmass model

MMASS model

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multilayered multi agent situated system mmass
Multilayered Multi Agent Situated System (MMASS)
  • MMASS  a constellation of interacting Multi Agent Situated Systems (MASS)

Construct(MASS1 … MASSn)

where MASSdenotes a Multi Agent Situated System (MASS)

  • <Space, F, A>
    • Space: spatial structure of a layer of agent environment
    • F: set of fields propagating throughout the Space
    • A: set of situated agents

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agent structured environment
Agent Structured Environment
  • Multilayered space  set of interacting spaces
  • Space: set P of sites arranged in a network
  • Each site pP is defined by <ap, Fp, Pp> where
    • PpP: set of sites adjacent to p
    • apA {}: agent situated in p
    • FpF: set of fields active in p

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fields at a distance and asynchronous interaction
Fields – at-a-distance and asynchronous interaction
  • Fields are
    • generated by agents
    • propagated throughout the space
    • perceived by other agents
  • <Wf, Diffusionf, Comparef, Composef>
    • Wf: set of field values
    • Diffusionf: P X Wf X P Wf X…XWf
    • Composef: Wf …XWf Wf
    • Comparef: Wf X Wf {True, False}

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situated agents
Situated Agents

3

4

4

2

1

a

  • aA : <s,p,T> (s current state, p current position)
  • T  < T, PerceptionT, ActionT>
    • T: set of states that agents can assume
    • PerceptionT: T [N X Wf1] …[N X Wf|F|]

PerceptionT(s) = (cT(s), tT(s))

      • cT(s): coefficient applied to field values
      • tT(s): sensibility threshold to fields
      • An agent perceives a field fi when CompareT(ciT(s)…wfi,tiT(s)) is True
    • ActionT: set of allowed actions for agents of type T

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language for mmass l mass

Language for MMASS(L*MASS)

to express actions

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trigger
state(s): the agent state is s

perceive(fi): the field fi is active in p (fiFp) and agents of type T in state s can perceive it and (Compare(ciT(s)*wfi, tiT(s)=True)

The effect is the change of agent state

trigger(…)

action: trigger(s,fi,s’)

condit: state(s), perceive(fi)

effect: state(s’)

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transport
position(s): the agent is situated in s

empty(q), near(p,q): q is a site adjacent to p (qPp) and no agent is situated in it (q=< ,Fq,Pq>)

perceive(fi): the field fi is active in p and the agent can perceive it

The effect is the change of agent position

transport(…)

action: transport(p,fi,q)

condit: position(p), empty(q), near(p,q), perceive(fi)

effect: position(q), empty(p)

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slide15
state(s): the agent state is s

present(f,p): a field f is active where the agent is situated f Fp

The effect is the emission of a new field to be diffused throughout the space

emit(…)

action: emit(s,f,p)

condit: state(s)

effect: present(f, p’) for all p’  P

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reaction
state(s): the agent state is s

agreed(ap1, ap2,…, apn): agents situated in sites {p1,p2,…,pn}Pp have previously agreed to undertake a synchronous reaction

The effect is the synchronous state change of the involved agents

reaction(…)

action: reaction(s, ap1, ap2, …, apn,s’)

condit: state(s), agreed(ap1, ap2,…, apn)

effect: state(s’)

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