2) Sparc2 was programmed in C , a high-level language, and MARS was programmed in assembly language, which made full use of the hardware features of the MARS processors like bit field extraction and manipulation, table access, and interprocessor communication support. The key contribution of this short note is the partitioning of the Goldberg-Tarjan network flow algorithm for pipelined executio...

In this paper, we investigate multi-agent learning (MAL) in a multi-agent resource selection problem (MARS) in which a large group of agents are competing for common resources. Since agents in such a setting are self-interested, MAL in MARS domains typically focuses on the convergence to a set of non-cooperative equilibria. As seen in the example of prisoner’s dilemma, however, selfish equilibr...

The accurate recognition of impact craters is important to analyze and understand the relative dating of Martian surface. Since manually identifying small craters in a deluge of high-resolution Martian images is a tremendous task, a robust automatic detection algorithm of the crater is needed, but subject to lots of uncertainties and low successful detection rates. In this paper, a modified ada...

The Military Analogical Reasoning System (MARS) is a performance support system and decision aid for commanders in Tactical Operations Centers. MARS enhances and supports the innate human ability for using stories to reason about tactical goals, plans, situations, and outcomes. The system operates by comparing many instances of stored tactical stories, determining which have analogous situation...

This paper presents a novel region discovery framework geared towards finding scientifically interesting places in spatial datasets. We view region discovery as a clustering problem in which an externally given fitness function has to be maximized. The framework adapts four representative clustering algorithms, exemplifying prototype-based, gridbased, density-based, and agglomerative clustering...

This study examines the capability of the Relevance Vector Machine (RVM) and Multivariate Adaptive Regression Spline (MARS) for prediction of ultimate capacity of driven piles and drilled shafts. RVM is a sparse method for training generalized linear models, while MARS technique is basically an adaptive piece-wise regression approach. In this paper, pile capacity prediction models are developed...

In the present study, the multivariate adaptive regression splines (MARS) technique is employed to estimate the drying shrinkage of concrete. To this purpose, a very big database (RILEM Data Bank) from different experimental studies is used. Several effective parameters such as the age of onset of shrinkage measurement, age at start of drying, the ratio of the volume of the sample on its drying...

This paper investigates the use of state-dependent Riccati equation control for closed-loop guidance of the hypersonic phase of atmospheric entry. Included are a discussion of the development of the state-dependent Riccati equations, their outgrowth from Hamilton-Jacobi-Bellman theory, a discussion of the closed-loop nonlinear system’s closed-loop stability and robustness from both a theoretica...