1. A method for actuating a machine, comprising: determining, by a processing unit, a solution using relaxed abduction for a defined problem related to incorrect information or incomplete models associated with diagnosis of the machine using automatically joint optimization of sets of explained observations and required assumptions to determine a relaxed abduction problem, solving, by a processing unit, the relaxed abduction problem so that the machine is actuated, wherein the relaxed abduction problem is determined by description logic, and actuating, by a processing unit, the machine for the diagnosis, wherein two orders of preference over a subset of the observations and a subset of the assumptions are taken as a basis for determining tuples, so that the theory together with the subset of the assumptions explains the subset of the observations; wherein the relaxed abduction problem is solved by transforming the relaxed abduction problem into a hypergraph, so that tuples (A,O) are encoded by pareto-optimal paths in the hypergraph; wherein hyperedges of the hypergraph are induced by transcriptions of prescribed rules; and wherein a weighted hypergraph H RAP =(V,E), which is induced by the relaxed abduction problem, is determined by V = { ( A ⊑ B ) , ( A ⊑ ∃ r · B ) | A , B ∈ N C T , r ∈ N R } , ⁢ wherein V T = { ( A ⊑ A ) , ( A ⊑ T ) | A ∈ N C T } ⊆ V denotes a set of final states and E denotes a set of the hyperedges e = ( T ⁡ ( e ) , h ⁡ ( e ) , w ⁡ ( e ) ) , so that the following holds: there is an axiom aεT∪A that justifies the derivation h(e)εV from T(e) ⊂ V on the basis of one of the prescribed rules, wherein the edge weight w(e) is determined according to A = { { a } if ⁢ ⁢ a ⁢ ∈ ⁢ A , ⁢ ∅ otherwise ⁢ ⁢ O = { { h ⁡ ( e ) } if ⁢ ⁢ h ⁡ ( e ) ∈ 𝒪 , ∅ otherwise .
1. A method for actuating a machine, comprising: determining, by a processing unit, a solution using relaxed abduction for a defined problem related to incorrect information or incomplete models associated with diagnosis of the machine using automatically joint optimization of sets of explained observations and required assumptions to determine a relaxed abduction problem, solving, by a processing unit, the relaxed abduction problem so that the machine is actuated, wherein the relaxed abduction problem is determined by description logic, and actuating, by a processing unit, the machine for the diagnosis, wherein two orders of preference over a subset of the observations and a subset of the assumptions are taken as a basis for determining tuples, so that the theory together with the subset of the assumptions explains the subset of the observations; wherein the relaxed abduction problem is solved by transforming the relaxed abduction problem into a hypergraph, so that tuples (A,O) are encoded by pareto-optimal paths in the hypergraph; wherein hyperedges of the hypergraph are induced by transcriptions of prescribed rules; and wherein a weighted hypergraph H RAP =(V,E), which is induced by the relaxed abduction problem, is determined by V = { ( A ⊑ B ) , ( A ⊑ ∃ r · B ) | A , B ∈ N C T , r ∈ N R } , ⁢ wherein V T = { ( A ⊑ A ) , ( A ⊑ T ) | A ∈ N C T } ⊆ V denotes a set of final states and E denotes a set of the hyperedges e = ( T ⁡ ( e ) , h ⁡ ( e ) , w ⁡ ( e ) ) , so that the following holds: there is an axiom aεT∪A that justifies the derivation h(e)εV from T(e) ⊂ V on the basis of one of the prescribed rules, wherein the edge weight w(e) is determined according to A = { { a } if ⁢ ⁢ a ⁢ ∈ ⁢ A , ⁢ ∅ otherwise ⁢ ⁢ O = { { h ⁡ ( e ) } if ⁢ ⁢ h ⁡ ( e ) ∈ 𝒪 , ∅ otherwise . 4. The method as claimed in claim 1 , wherein the pareto-optimal paths are determined by a label approach.