6,764 research outputs found
Monad Transformers for Backtracking Search
This paper extends Escardo and Oliva's selection monad to the selection monad
transformer, a general monadic framework for expressing backtracking search
algorithms in Haskell. The use of the closely related continuation monad
transformer for similar purposes is also discussed, including an implementation
of a DPLL-like SAT solver with no explicit recursion. Continuing a line of work
exploring connections between selection functions and game theory, we use the
selection monad transformer with the nondeterminism monad to obtain an
intuitive notion of backward induction for a certain class of nondeterministic
games.Comment: In Proceedings MSFP 2014, arXiv:1406.153
Learning in Real-Time Search: A Unifying Framework
Real-time search methods are suited for tasks in which the agent is
interacting with an initially unknown environment in real time. In such
simultaneous planning and learning problems, the agent has to select its
actions in a limited amount of time, while sensing only a local part of the
environment centered at the agents current location. Real-time heuristic search
agents select actions using a limited lookahead search and evaluating the
frontier states with a heuristic function. Over repeated experiences, they
refine heuristic values of states to avoid infinite loops and to converge to
better solutions. The wide spread of such settings in autonomous software and
hardware agents has led to an explosion of real-time search algorithms over the
last two decades. Not only is a potential user confronted with a hodgepodge of
algorithms, but he also faces the choice of control parameters they use. In
this paper we address both problems. The first contribution is an introduction
of a simple three-parameter framework (named LRTS) which extracts the core
ideas behind many existing algorithms. We then prove that LRTA*, epsilon-LRTA*,
SLA*, and gamma-Trap algorithms are special cases of our framework. Thus, they
are unified and extended with additional features. Second, we prove
completeness and convergence of any algorithm covered by the LRTS framework.
Third, we prove several upper-bounds relating the control parameters and
solution quality. Finally, we analyze the influence of the three control
parameters empirically in the realistic scalable domains of real-time
navigation on initially unknown maps from a commercial role-playing game as
well as routing in ad hoc sensor networks
A Random Walk Perspective on Hide-and-Seek Games
We investigate hide-and-seek games on complex networks using a random walk
framework. Specifically, we investigate the efficiency of various degree-biased
random walk search strategies to locate items that are randomly hidden on a
subset of vertices of a random graph. Vertices at which items are hidden in the
network are chosen at random as well, though with probabilities that may depend
on degree. We pitch various hide and seek strategies against each other, and
determine the efficiency of search strategies by computing the average number
of hidden items that a searcher will uncover in a random walk of steps. Our
analysis is based on the cavity method for finite single instances of the
problem, and generalises previous work of De Bacco et al. [1] so as to cover
degree-biased random walks. We also extend the analysis to deal with the
thermodynamic limit of infinite system size. We study a broad spectrum of
functional forms for the degree bias of both the hiding and the search strategy
and investigate the efficiency of families of search strategies for cases where
their functional form is either matched or unmatched to that of the hiding
strategy. Our results are in excellent agreement with those of numerical
simulations. We propose two simple approximations for predicting efficient
search strategies. One is based on an equilibrium analysis of the random walk
search strategy. While not exact, it produces correct orders of magnitude for
parameters characterising optimal search strategies. The second exploits the
existence of an effective drift in random walks on networks, and is expected to
be efficient in systems with low concentration of small degree nodes.Comment: 31 pages, 10 (multi-part) figure
On Backtracking in Real-time Heuristic Search
Real-time heuristic search algorithms are suitable for situated agents that
need to make their decisions in constant time. Since the original work by Korf
nearly two decades ago, numerous extensions have been suggested. One of the
most intriguing extensions is the idea of backtracking wherein the agent
decides to return to a previously visited state as opposed to moving forward
greedily. This idea has been empirically shown to have a significant impact on
various performance measures. The studies have been carried out in particular
empirical testbeds with specific real-time search algorithms that use
backtracking. Consequently, the extent to which the trends observed are
characteristic of backtracking in general is unclear. In this paper, we present
the first entirely theoretical study of backtracking in real-time heuristic
search. In particular, we present upper bounds on the solution cost exponential
and linear in a parameter regulating the amount of backtracking. The results
hold for a wide class of real-time heuristic search algorithms that includes
many existing algorithms as a small subclass
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