597 research outputs found
Analysis of Generalized Artificial Intelligence Potential through Reinforcement and Deep Reinforcement Learning Approaches
Artificial Intelligence is the next competitive domain; the first nation to develop human level artificial intelligence will have an impact similar to the development of the atomic bomb. To maintain the security of the United States and her people, the Department of Defense has funded research into the development of artificial intelligence and its applications. This research uses reinforcement learning and deep reinforcement learning methods as proxies for current and future artificial intelligence agents and to assess potential issues in development. Agent performance were compared across two games and one excursion: Cargo Loading, Tower of Hanoi, and Knapsack Problem, respectively. Deep reinforcement learning agents were observed to handle a wider range of problems, but behave inferior to specialized reinforcement learning algorithms
On Rearrangement of Items Stored in Stacks
There are stacks, each filled with items, and one empty stack.
Every stack has capacity . A robot arm, in one stack operation (step),
may pop one item from the top of a non-empty stack and subsequently push it
onto a stack not at capacity. In a {\em labeled} problem, all items are
distinguishable and are initially randomly scattered in the stacks. The
items must be rearranged using pop-and-pushs so that in the end, the stack holds items , in that order, from the top to
the bottom for all . In an {\em unlabeled} problem, the
items are of types of each. The goal is to rearrange items so that
items of type are located in the stack for all . In carrying out the rearrangement, a natural question is to find the least
number of required pop-and-pushes.
Our main contributions are: (1) an algorithm for restoring the order of
items stored in an table using only column and row
permutations, and its generalization, and (2) an algorithm with a guaranteed
upper bound of steps for solving both versions of the stack
rearrangement problem when for arbitrary fixed
positive number . In terms of the required number of steps, the labeled and
unlabeled version have lower bounds
and , respectively
Loopless Gray Code Enumeration and the Tower of Bucharest
We give new algorithms for generating all n-tuples over an alphabet of m letters, changing only one letter at a time (Gray codes). These algorithms are based on the connection with variations of the Towers of Hanoi game. Our algorithms are loopless, in the sense that the next change can be determined in a constant number of steps, and they can be implemented in hardware. We also give another family of loopless algorithms that is based on the idea of working ahead and saving the work in a buffer
On the restricted Hanoi Graphs
Consider the restricted Hanoi graphs which correspond to the variants of the
famous Tower of Hanoi problem with multiple pegs where moves of the discs are
restricted throughout the arcs of a movement digraph whose vertices represent
the pegs of the puzzle and an arc from vertex to vertex exists if and
only if moves from peg to peg are allowed. In this paper, we gave some
notes on how to construct the restricted Hanoi graphs as well as some
combinatorial results on the number of arcs in these graphs.Comment: 8 pages, 2 figure
Boosting Multi-Core Reachability Performance with Shared Hash Tables
This paper focuses on data structures for multi-core reachability, which is a
key component in model checking algorithms and other verification methods. A
cornerstone of an efficient solution is the storage of visited states. In
related work, static partitioning of the state space was combined with
thread-local storage and resulted in reasonable speedups, but left open whether
improvements are possible. In this paper, we present a scaling solution for
shared state storage which is based on a lockless hash table implementation.
The solution is specifically designed for the cache architecture of modern
CPUs. Because model checking algorithms impose loose requirements on the hash
table operations, their design can be streamlined substantially compared to
related work on lockless hash tables. Still, an implementation of the hash
table presented here has dozens of sensitive performance parameters (bucket
size, cache line size, data layout, probing sequence, etc.). We analyzed their
impact and compared the resulting speedups with related tools. Our
implementation outperforms two state-of-the-art multi-core model checkers (SPIN
and DiVinE) by a substantial margin, while placing fewer constraints on the
load balancing and search algorithms.Comment: preliminary repor
Front-to-End Bidirectional Heuristic Search with Near-Optimal Node Expansions
It is well-known that any admissible unidirectional heuristic search
algorithm must expand all states whose -value is smaller than the optimal
solution cost when using a consistent heuristic. Such states are called "surely
expanded" (s.e.). A recent study characterized s.e. pairs of states for
bidirectional search with consistent heuristics: if a pair of states is s.e.
then at least one of the two states must be expanded. This paper derives a
lower bound, VC, on the minimum number of expansions required to cover all s.e.
pairs, and present a new admissible front-to-end bidirectional heuristic search
algorithm, Near-Optimal Bidirectional Search (NBS), that is guaranteed to do no
more than 2VC expansions. We further prove that no admissible front-to-end
algorithm has a worst case better than 2VC. Experimental results show that NBS
competes with or outperforms existing bidirectional search algorithms, and
often outperforms A* as well.Comment: Accepted to IJCAI 2017. Camera ready version with new timing result
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