AbstractA planning problem is k-dependent if each action has at most k pre-conditions on variables unaffected by the action. This concept is of interest because k is a constant for all but a few of the current benchmark domains in planning, and is known to have implications for tractability. In this paper, we present an algorithm for solving planning problems in P(k), the class of k-dependent planning problems with binary variables and polytree causal graphs. We prove that our algorithm runs in polynomial time when k is a fixed constant. If, in addition, the causal graph has bounded depth, we show that plan generation is linear in the size of the input. Although these contributions are theoretical due to the limited scope of the class P(k),...
Unary operator domains – i.e., domains in which operators have a single effect – arise naturally in ...
We consider planning problems for graphs, Markov decision processes (MDPs), and games on graphs. Whi...
We consider planning problems for graphs, Markov decision processes (MDPs), and games on graphs. Whi...
A planning problem is k-dependent if each action has at most k pre-conditions on variables unaffecte...
A planning problem is k-dependent if each action has at most k pre-conditions on variables unaffecte...
AbstractA planning problem is k-dependent if each action has at most k pre-conditions on variables u...
We present three new complexity results for classes of plan-ning problems with simple causal graphs....
We present three new complexity results for classes of planning problems with simple causal graphs. ...
AbstractThe causal graph is a directed graph that describes the variable dependencies present in a p...
The causal graph is a directed graph that describes the vari-able dependencies present in a planning...
Complexity analysis based on the causal graphs of planning instances is a highly important research ...
Causal graphs are widely used to analyze the complexity of planning problems. Many tractable classes...
Automated planning is known to be computationally hard in the general case. Propositional planning i...
Automated planning is known to be computationally hard in the general case. Propositional planning i...
Causal graphs are widely used in planning to capture the internal structure of planning instances. ...
Unary operator domains – i.e., domains in which operators have a single effect – arise naturally in ...
We consider planning problems for graphs, Markov decision processes (MDPs), and games on graphs. Whi...
We consider planning problems for graphs, Markov decision processes (MDPs), and games on graphs. Whi...
A planning problem is k-dependent if each action has at most k pre-conditions on variables unaffecte...
A planning problem is k-dependent if each action has at most k pre-conditions on variables unaffecte...
AbstractA planning problem is k-dependent if each action has at most k pre-conditions on variables u...
We present three new complexity results for classes of plan-ning problems with simple causal graphs....
We present three new complexity results for classes of planning problems with simple causal graphs. ...
AbstractThe causal graph is a directed graph that describes the variable dependencies present in a p...
The causal graph is a directed graph that describes the vari-able dependencies present in a planning...
Complexity analysis based on the causal graphs of planning instances is a highly important research ...
Causal graphs are widely used to analyze the complexity of planning problems. Many tractable classes...
Automated planning is known to be computationally hard in the general case. Propositional planning i...
Automated planning is known to be computationally hard in the general case. Propositional planning i...
Causal graphs are widely used in planning to capture the internal structure of planning instances. ...
Unary operator domains – i.e., domains in which operators have a single effect – arise naturally in ...
We consider planning problems for graphs, Markov decision processes (MDPs), and games on graphs. Whi...
We consider planning problems for graphs, Markov decision processes (MDPs), and games on graphs. Whi...