Multi layered multi agent situated system
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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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Multi layered multi agent situated system

Multi-layered Multi-agent Situated System



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)


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


M ma s s

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


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


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()


Mmass model

MMASS model


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


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


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}


Situated agents
Situated Agents







  • 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


Language for mmass l mass

Language for MMASS(L*MASS)

to express actions



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


action: trigger(s,fi,s’)

condit: state(s), perceive(fi)

effect: state(s’)



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


action: transport(p,fi,q)

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

effect: position(q), empty(p)


Multi layered multi agent situated system

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


action: emit(s,f,p)

condit: state(s)

effect: present(f, p’) for all p’  P



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


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

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

effect: state(s’)