By Markus Hannebauer

High verbal exchange efforts and terrible challenge fixing effects as a result of limited evaluate are relevant concerns in collaborative challenge fixing. This paintings addresses those concerns via introducing the procedures of agent melting and agent splitting that let person challenge fixing brokers to continually and autonomously reconfigure and adapt themselves to the actual challenge to be solved.

The writer presents a valid theoretical origin of collaborative challenge fixing itself and introduces quite a few new layout ideas and methods to enhance its caliber and potency, reminiscent of the multi-phase contract discovering protocol for exterior challenge fixing, the composable belief-desire-intention agent structure, and the distribution-aware constraint specification structure for inner challenge solving.

The useful relevance and applicability of the thoughts and methods supplied are verified through the use of scientific appointment scheduling as a case study.

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Extra resources for Autonomous Dynamic Reconfiguration in Multi-Agent Systems: Improving the Quality and Efficiency of Collaborative Problem Solving

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We further assume that as soon as a set of intelligent entities has accepted a problem to be a common problem, it willingly accepts collaboration to solve it. This is why the concept of collaborative problem solving seems to fit better in closed systems where common problems can easily be defined from the closed system objective. But collaborative problem solving is not just a concept for closed systems. As already discussed, in open systems common problems may arise as well, but the motivation for accepting a problem as common is more difficult to find.

Just as we have introduced τ -solution space equivalence between constraint problems, which corresponds to “equals”, we can introduce “larger or equal” and “less or equal”. 6 (τ -Solution Space Reducibility). Given two constraint problems Π1 and Π2 and a transformation τ ⊆ Λ(Π1 ) × Λ(Π2 ), Π1 is τ -solution space reducible to Π2 (Π1 ≥τ Π2 ), iff Σ(Π2 ) ⊆ τ (Σ(Π1 )) ∧ Σ(Π1 ) ⊇ τ −1 (Σ(Π2 )). 7 (τ -Solution Space Extensibility). Given two constraint problems Π1 and Π2 and a transformation τ ⊆ Λ(Π1 ) × Λ(Π2 ), Π1 is τ -solution space extensible to Π2 (Π1 ≤τ Π2 ), iff Σ(Π2 ) ⊇ τ (Σ(Π1 )) ∧ Σ(Π1 ) ⊆ τ −1 (Σ(Π2 )).

Id is reflexive, symmetric and transitive. Just as we have introduced τ -solution space equivalence between constraint problems, which corresponds to “equals”, we can introduce “larger or equal” and “less or equal”. 6 (τ -Solution Space Reducibility). Given two constraint problems Π1 and Π2 and a transformation τ ⊆ Λ(Π1 ) × Λ(Π2 ), Π1 is τ -solution space reducible to Π2 (Π1 ≥τ Π2 ), iff Σ(Π2 ) ⊆ τ (Σ(Π1 )) ∧ Σ(Π1 ) ⊇ τ −1 (Σ(Π2 )). 7 (τ -Solution Space Extensibility). Given two constraint problems Π1 and Π2 and a transformation τ ⊆ Λ(Π1 ) × Λ(Π2 ), Π1 is τ -solution space extensible to Π2 (Π1 ≤τ Π2 ), iff Σ(Π2 ) ⊇ τ (Σ(Π1 )) ∧ Σ(Π1 ) ⊆ τ −1 (Σ(Π2 )).

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