The induction of classification rules has been dominated by a single generic technique—the covering algorithm. This approach employs a simple hill-climbing search to learn sets of rules. Such search is subject to numerous widely known deficiencies. Further, there is a growing body of evidence that learning redundant sets of rules can improve predictive accuracy. The ultimate end-point of a move...

We present a new manufacturing technology to produce multi-scale compliant feet for use with a novel climbing robot. Climbing robots for space exploration missions may allow scientists to explore environments too difficult for traditional wheeled designs. These climbing robot designs should be able to grasp or adhere to a variety of surface types at multiple angles in order to be effective. To ...

Hill climbing is used to maximize an information theoretic measure of the difference between the actual behavior of a unit and the behavior that would be predicted by a statistician who knew the first order statistics of the inputs but believed them to be independent. This causes the unit to detect higher order correlations among its inputs. Initial simulations are presented, and seem encouragi...

We empirically investigate the properties of the search space and the behavior of hill-climbing search for solving hard, random Boolean satis ability problems. In these experiments it was frequently observed that rather than attempting to escape from plateaus by extensive search, it was better to completely restart from a new random initial state. The optimumpoint to terminate search and restar...

The use of macro-actions in planning introduces a trade-off. Macro-actions can offer search guidance by suggesting sequences of actions; but can potentially make search more expensive by increasing the branching factor. In this paper we present a technique for simulating the use of macro-actions by altering the order in which actions are considered for application during enforced hill-climbing ...

We evaluate different call admission control policies in various multiservice cellular scenarios. For each of the studied policies we obtain the maximum calling rate that can be offered to the system to achieve a given QoS objective defined in terms of blocking probabilities. We propose an optimization methodology based on a hill climbing algorithm to find the optimum configuration for most pol...

In this paper, we present an approach for mobile robot localization designed for use in dynamic environments. Our approach integrates evidence grids within a topo-logical/metric network that can be used for navigation. Place learning consists of associating evidence grids with places in the topological network. Place recognition consists of building an evidence grid at the current location and ...

This paper investigates the problem of policy learning in multi-agent environments using the stochastic game framework, which we brieey overview. We introduce two properties as desirable for a learning agent when in the presence of other learning agents, namely rationality and convergence. We examine existing reinforcement learning algorithms according to these two properties and notice that th...

Analog Gradient Search This analog phase demodulation hill-climbing technique works for clean, quasi-Gaussian couplings already near optimal alignment. However it will lock-in on local maxima and “flat spots” which occur in multimode couplings, with imperfect devices, or far from optimum alignment. Nevertheless, instrumentation based on this principle was popular, though bulky and providing too...

Nowadays there are a huge number of applications produce the immense amount of data in the form of a data stream, which needs real time analysis. Sensor networks, real-time surveillance and telecommunication systems are the examples of such applications. The real time analysis of the data stream leads to a number of computational and mining challenges. In this scenario new data arrives continuo...