EfficientIDC: A Faster Incremental Dynamic Controllability Algorithm
... to itself) represents a trivially satisfied constraint that can be skipped. A negative loop entails that a node must be executed before itself, which violates DC and is reported. If ei is not a loop, FastIDC determines whether one or more of the derivation rules in figure 2 can be applied with ei as ...
... to itself) represents a trivially satisfied constraint that can be skipped. A negative loop entails that a node must be executed before itself, which violates DC and is reported. If ei is not a loop, FastIDC determines whether one or more of the derivation rules in figure 2 can be applied with ei as ...
Planning with h+ in Theory and Practice
... the number of subsets chosen, and hence short relaxed plans correspond to small set covers. Theorem 2 shows that we cannot hope to find a polynomial algorithm that is guaranteed to find good approximations to h+ . However, since theoretical results of this kind tend to rely on somewhat pathological ...
... the number of subsets chosen, and hence short relaxed plans correspond to small set covers. Theorem 2 shows that we cannot hope to find a polynomial algorithm that is guaranteed to find good approximations to h+ . However, since theoretical results of this kind tend to rely on somewhat pathological ...
45 Online Planning for Large Markov Decision Processes
... space before the agent is actually interacting with the environment. In practice, offline algorithms often suffer from the problem of scalability due to the well-known “curse of dimensionality”—that is, the size of state space grows exponentially with the number of state variables [Littman et al. 19 ...
... space before the agent is actually interacting with the environment. In practice, offline algorithms often suffer from the problem of scalability due to the well-known “curse of dimensionality”—that is, the size of state space grows exponentially with the number of state variables [Littman et al. 19 ...
Limitations of Front-to-End Bidirectional Heuristic Search
... dashed lines. The algorithm used is breadth-first heuristic search (BFHS) (Zhou and Hansen 2006), which expands nodes in breadth-first order while maintaining a global cost cutoff. Any node whose f cost exceeds that cutoff is pruned. We set the cutoff to the optimal solution cost. We used increasing ...
... dashed lines. The algorithm used is breadth-first heuristic search (BFHS) (Zhou and Hansen 2006), which expands nodes in breadth-first order while maintaining a global cost cutoff. Any node whose f cost exceeds that cutoff is pruned. We set the cutoff to the optimal solution cost. We used increasing ...
SetA*: An Efficient BDD-Based Heuristic Search Algorithm
... NM Set A* behaves like A*. For it performs best-first search, and it carries out a regular breadth-first search. The for fact that can take any value in the range is important in practice, since it can be used to increase an underestimating heuristic or decrease an overestimating heuristic. We end t ...
... NM Set A* behaves like A*. For it performs best-first search, and it carries out a regular breadth-first search. The for fact that can take any value in the range is important in practice, since it can be used to increase an underestimating heuristic or decrease an overestimating heuristic. We end t ...
Distributed Stochastic Search for Constraint Satisfaction and Optimization:
... must be as small as possible, as long as it maintains coverage of the overall area. Second, two signals whose strengths are above a certain threshold at a sensor location will interfere with each other. This constraint, therefore, requires that the signaling actions of two overlapping ping nodes be ...
... must be as small as possible, as long as it maintains coverage of the overall area. Second, two signals whose strengths are above a certain threshold at a sensor location will interfere with each other. This constraint, therefore, requires that the signaling actions of two overlapping ping nodes be ...
Algorithm Selection for Combinatorial Search Problems: A Survey
... clearly superior to previous approaches. In the majority of cases however, a new approach will improve over the current state of the art for only some problem instances. This may be because it employs a heuristic that fails for instances of a certain type or because it makes other assumptions about ...
... clearly superior to previous approaches. In the majority of cases however, a new approach will improve over the current state of the art for only some problem instances. This may be because it employs a heuristic that fails for instances of a certain type or because it makes other assumptions about ...
AIRS: Anytime Iterative Refinement of a Solution
... is beneficial so that when the clock runs out, the probability of wasting time on an “almost completed refinement” is minimal. (This can be a problem for WA*, in which consecutive searches take longer and longer as ϵ grows). Still, the AIRS algorithm—like any algorithm—has important trade-offs to make. ...
... is beneficial so that when the clock runs out, the probability of wasting time on an “almost completed refinement” is minimal. (This can be a problem for WA*, in which consecutive searches take longer and longer as ϵ grows). Still, the AIRS algorithm—like any algorithm—has important trade-offs to make. ...
An Extension of the ICP Algorithm Considering Scale Factor
... adding this constraint condition is to avoid the phenomenon happening that points of a set converge to a point of the other set. This constraint optimization problem is solved by the Scaling Iterative Closest Point (SICP) algorithm which is an extension of the ICP algorithm. At each iterative step o ...
... adding this constraint condition is to avoid the phenomenon happening that points of a set converge to a point of the other set. This constraint optimization problem is solved by the Scaling Iterative Closest Point (SICP) algorithm which is an extension of the ICP algorithm. At each iterative step o ...
