7,273 research outputs found
Bad Universal Priors and Notions of Optimality
A big open question of algorithmic information theory is the choice of the
universal Turing machine (UTM). For Kolmogorov complexity and Solomonoff
induction we have invariance theorems: the choice of the UTM changes bounds
only by a constant. For the universally intelligent agent AIXI (Hutter, 2005)
no invariance theorem is known. Our results are entirely negative: we discuss
cases in which unlucky or adversarial choices of the UTM cause AIXI to
misbehave drastically. We show that Legg-Hutter intelligence and thus balanced
Pareto optimality is entirely subjective, and that every policy is Pareto
optimal in the class of all computable environments. This undermines all
existing optimality properties for AIXI. While it may still serve as a gold
standard for AI, our results imply that AIXI is a relative theory, dependent on
the choice of the UTM.Comment: COLT 201
Optimal and Myopic Information Acquisition
We consider the problem of optimal dynamic information acquisition from many
correlated information sources. Each period, the decision-maker jointly takes
an action and allocates a fixed number of observations across the available
sources. His payoff depends on the actions taken and on an unknown state. In
the canonical setting of jointly normal information sources, we show that the
optimal dynamic information acquisition rule proceeds myopically after finitely
many periods. If signals are acquired in large blocks each period, then the
optimal rule turns out to be myopic from period 1. These results demonstrate
the possibility of robust and "simple" optimal information acquisition, and
simplify the analysis of dynamic information acquisition in a widely used
informational environment
An Asymptotically-Optimal Sampling-Based Algorithm for Bi-directional Motion Planning
Bi-directional search is a widely used strategy to increase the success and
convergence rates of sampling-based motion planning algorithms. Yet, few
results are available that merge both bi-directional search and asymptotic
optimality into existing optimal planners, such as PRM*, RRT*, and FMT*. The
objective of this paper is to fill this gap. Specifically, this paper presents
a bi-directional, sampling-based, asymptotically-optimal algorithm named
Bi-directional FMT* (BFMT*) that extends the Fast Marching Tree (FMT*)
algorithm to bi-directional search while preserving its key properties, chiefly
lazy search and asymptotic optimality through convergence in probability. BFMT*
performs a two-source, lazy dynamic programming recursion over a set of
randomly-drawn samples, correspondingly generating two search trees: one in
cost-to-come space from the initial configuration and another in cost-to-go
space from the goal configuration. Numerical experiments illustrate the
advantages of BFMT* over its unidirectional counterpart, as well as a number of
other state-of-the-art planners.Comment: Accepted to the 2015 IEEE Intelligent Robotics and Systems Conference
in Hamburg, Germany. This submission represents the long version of the
conference manuscript, with additional proof details (Section IV) regarding
the asymptotic optimality of the BFMT* algorith
Sampling-based Algorithms for Optimal Motion Planning
During the last decade, sampling-based path planning algorithms, such as
Probabilistic RoadMaps (PRM) and Rapidly-exploring Random Trees (RRT), have
been shown to work well in practice and possess theoretical guarantees such as
probabilistic completeness. However, little effort has been devoted to the
formal analysis of the quality of the solution returned by such algorithms,
e.g., as a function of the number of samples. The purpose of this paper is to
fill this gap, by rigorously analyzing the asymptotic behavior of the cost of
the solution returned by stochastic sampling-based algorithms as the number of
samples increases. A number of negative results are provided, characterizing
existing algorithms, e.g., showing that, under mild technical conditions, the
cost of the solution returned by broadly used sampling-based algorithms
converges almost surely to a non-optimal value. The main contribution of the
paper is the introduction of new algorithms, namely, PRM* and RRT*, which are
provably asymptotically optimal, i.e., such that the cost of the returned
solution converges almost surely to the optimum. Moreover, it is shown that the
computational complexity of the new algorithms is within a constant factor of
that of their probabilistically complete (but not asymptotically optimal)
counterparts. The analysis in this paper hinges on novel connections between
stochastic sampling-based path planning algorithms and the theory of random
geometric graphs.Comment: 76 pages, 26 figures, to appear in International Journal of Robotics
Researc
Incremental Sampling-based Algorithms for Optimal Motion Planning
During the last decade, incremental sampling-based motion planning
algorithms, such as the Rapidly-exploring Random Trees (RRTs) have been shown
to work well in practice and to possess theoretical guarantees such as
probabilistic completeness. However, no theoretical bounds on the quality of
the solution obtained by these algorithms have been established so far. The
first contribution of this paper is a negative result: it is proven that, under
mild technical conditions, the cost of the best path in the RRT converges
almost surely to a non-optimal value. Second, a new algorithm is considered,
called the Rapidly-exploring Random Graph (RRG), and it is shown that the cost
of the best path in the RRG converges to the optimum almost surely. Third, a
tree version of RRG is introduced, called the RRT algorithm, which
preserves the asymptotic optimality of RRG while maintaining a tree structure
like RRT. The analysis of the new algorithms hinges on novel connections
between sampling-based motion planning algorithms and the theory of random
geometric graphs. In terms of computational complexity, it is shown that the
number of simple operations required by both the RRG and RRT algorithms is
asymptotically within a constant factor of that required by RRT.Comment: 20 pages, 10 figures, this manuscript is submitted to the
International Journal of Robotics Research, a short version is to appear at
the 2010 Robotics: Science and Systems Conference
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