+publisher:"Colorado State University" +contributor:("Howe, Adele E.")

Colorado State University

1. Urbanska, Malgorzata. Automated security analysis of the home computer.

Degree: MS(M.S.), Computer Science, 2014, Colorado State University

► Home computer users pose special challenges to the security of their machines. Often home computer users do not realize that their computer activities have repercussions… (more)

▼ Home computer users pose special challenges to the security of their machines. Often home computer users do not realize that their computer activities have repercussions on computer security. Frequently, they are not aware about their role in keeping their home computer secure. Therefore, security analysis solutions for a home computer must differ significantly from standard security solutions. In addition to considering the properties of a single system, the characteristics of a home user have to be deliberated. Attack Graphs (AGs) are models that have been widely used for security analysis. A Personalized Attack Graph (PAG) extends the traditional AGs for this purpose. It characterizes the interplay between vulnerabilities, user actions, attacker strategies, and system activities. Success of such security analysis depends on the level of detailed information used to build the PAG. Because the PAG can have hundreds of elements and manual analysis can be error-prone and tedious, automation of this process is an essential component in the security analysis for the home computer user. Automated security analysis, which applies the PAG, requires information about user behavior, attacker and system actions, and vulnerabilities that are present in the home computer. In this thesis, we expatiate on 1) modeling home user behavior in order to obtain user specific information, 2) analyzing vulnerability information resources to get the most detailed vulnerability descriptions, and 3) transforming vulnerability information into a format useful for automated construction of the PAG. We propose the Bayesian User Action model that quantitatively represents the relationships between different user characteristics and provides the likelihood of a user taking a specific cyber related action. This model complements the PAG by delivering information about the home user. We demonstrate how different user behavior affects exploit likelihood in the PAG. We compare different vulnerability information sources in order to identify the best source for security analysis of the home computer. We calculate contextual similarity of the vulnerability descriptions to identify the same vulnerabilities from different vulnerability databases. We measure the similarity of vulnerability descriptions of the same vulnerability from multiple sources in order to identify any additional information that can be used to construct the PAG. We demonstrate a methodology of transforming a textual vulnerability description into a more structured format. We use Information Extraction (IE) techniques that are based on regular expression rules and dictionaries of keywords. We extract five types of information: infected software, attacker/user/system preconditions, and postconditions of exploiting vulnerabilities. We evaluate the performance of our IE system by measuring accuracy for each type of extracted information. Experiments on influence of user profile on the PAG show that probability of exploits differ depending on user personality. Results also… Advisors/Committee Members: Ray, Indrajit (advisor), Howe, Adele E. (advisor), Byrne, Zinta (committee member).

Subjects/Keywords: attacks and defenses; attack graphs; security personalization; security risk modeling; system security; vulnerability database

Record Details Similar Records Cite « Share

❌

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Urbanska, M. (2014). Automated security analysis of the home computer. (Masters Thesis). Colorado State University. Retrieved from http://hdl.handle.net/10217/82538

Chicago Manual of Style (16^th Edition):

Urbanska, Malgorzata. “Automated security analysis of the home computer.” 2014. Masters Thesis, Colorado State University. Accessed March 09, 2021. http://hdl.handle.net/10217/82538.

MLA Handbook (7^th Edition):

Urbanska, Malgorzata. “Automated security analysis of the home computer.” 2014. Web. 09 Mar 2021.

Vancouver:

Urbanska M. Automated security analysis of the home computer. [Internet] [Masters thesis]. Colorado State University; 2014. [cited 2021 Mar 09]. Available from: http://hdl.handle.net/10217/82538.

Council of Science Editors:

Urbanska M. Automated security analysis of the home computer. [Masters Thesis]. Colorado State University; 2014. Available from: http://hdl.handle.net/10217/82538

Colorado State University

2. Hains, Douglas R. Generalized partition crossover for the traveling salesman problem.

Degree: MS(M.S.), Computer Science, 2011, Colorado State University

URL: http://hdl.handle.net/10217/47314

► The Traveling Salesman Problem (TSP) is a well-studied combinatorial optimization problem with a wide spectrum of applications and theoretical value. We have designed a new… (more)

Subjects/Keywords: genetic algorithms; Traveling Salesman Problem; search space; local search

Record Details Similar Records Cite « Share

❌

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Hains, D. R. (2011). Generalized partition crossover for the traveling salesman problem. (Masters Thesis). Colorado State University. Retrieved from http://hdl.handle.net/10217/47314

Chicago Manual of Style (16^th Edition):

Hains, Douglas R. “Generalized partition crossover for the traveling salesman problem.” 2011. Masters Thesis, Colorado State University. Accessed March 09, 2021. http://hdl.handle.net/10217/47314.

MLA Handbook (7^th Edition):

Hains, Douglas R. “Generalized partition crossover for the traveling salesman problem.” 2011. Web. 09 Mar 2021.

Vancouver:

Hains DR. Generalized partition crossover for the traveling salesman problem. [Internet] [Masters thesis]. Colorado State University; 2011. [cited 2021 Mar 09]. Available from: http://hdl.handle.net/10217/47314.

Council of Science Editors:

Hains DR. Generalized partition crossover for the traveling salesman problem. [Masters Thesis]. Colorado State University; 2011. Available from: http://hdl.handle.net/10217/47314

Colorado State University

3. Chen, Wenxiang. Step toward constant time local search for optimizing pseudo boolean functions, A.

Degree: MS(M.S.), Computer Science, 2013, Colorado State University

URL: http://hdl.handle.net/10217/81007

► Pseudo Boolean Functions (PBFs) are the objective functions for a wide class of hard optimization problems, such as MAX-SAT and MAX-CUT. Since these problems are… (more)

▼ Pseudo Boolean Functions (PBFs) are the objective functions for a wide class of hard optimization problems, such as MAX-SAT and MAX-CUT. Since these problems are NP-Hard, researchers and practitioners rely on incomplete solvers, such as Stochastic Local Search (SLS), for large problems. Best-Improvement Local Search (BILS) is a common form of SLS, which always takes the move yielding the highest improvement in the objective function. Generally, the more runtime SLS is given, the better solution can be obtained. This thesis aims at algorithmically accelerating SLS for PBFs using Walsh Analysis. The contributions of this thesis are threefold. First, a general approach for executing an approximate best-improvement move in constant time on average using Walsh analysis, "Walsh-LS", is described. Conventional BILS typically requires examining all n neighbors to decide which move to take, given the number of variables is n. With Walsh analysis, however, we can determine which neighbors need to be checked. As long as the objective function is epistatically bounded by a constant k (k is the number of variables per subfunctions), the number of neighbors that need to be checked is constant regardless of problem size. An impressive speedup of runtime (up to 449 times) is observed in our empirical studies. Second, in the context of Walsh-LS, we empirically study two key components of SLS from the perspectives of both efficiency and effectiveness: 1) Local optimum escape method: hard random or soft restarts; 2) Local search strategy: first-improvement or best-improvement. Lastly, on average we can perform approximate BILS using the mean over a Hamming region of arbitrary radius as a surrogate objective function. Even though the number of points is exponential in the radius of the Hamming region, BILS using the mean value of points in the Hamming region as a surrogate objective function can still take each move in time independent of n on average. According to our empirical studies, using the average over a Hamming region as a surrogate objective function can yield superior performance results on neutral landscapes like NKq-landscapes. Advisors/Committee Members: Whitley, L. Darrell (advisor), Howe, Adele E. (advisor), Cheney, Margaret (committee member).

Subjects/Keywords: complexity per move; stochastic local search; pseudo Boolean optimization; NK-landscapes

Record Details Similar Records Cite « Share

❌

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Chen, W. (2013). Step toward constant time local search for optimizing pseudo boolean functions, A. (Masters Thesis). Colorado State University. Retrieved from http://hdl.handle.net/10217/81007

Chicago Manual of Style (16^th Edition):

Chen, Wenxiang. “Step toward constant time local search for optimizing pseudo boolean functions, A.” 2013. Masters Thesis, Colorado State University. Accessed March 09, 2021. http://hdl.handle.net/10217/81007.

MLA Handbook (7^th Edition):

Chen, Wenxiang. “Step toward constant time local search for optimizing pseudo boolean functions, A.” 2013. Web. 09 Mar 2021.

Vancouver:

Chen W. Step toward constant time local search for optimizing pseudo boolean functions, A. [Internet] [Masters thesis]. Colorado State University; 2013. [cited 2021 Mar 09]. Available from: http://hdl.handle.net/10217/81007.

Council of Science Editors:

Chen W. Step toward constant time local search for optimizing pseudo boolean functions, A. [Masters Thesis]. Colorado State University; 2013. Available from: http://hdl.handle.net/10217/81007

Colorado State University

4. Barber, Michael J. Classification ensemble methods for mitigating concept drift within online data streams.

Degree: MS(M.S.), Computer Science, 2012, Colorado State University

URL: http://hdl.handle.net/10217/67994

► The task of instance classification within very large data streams is challenged by both the overwhelming amount of data, and a phenomenon known as concept… (more)

Subjects/Keywords: data mining; online analysis; machine learning; ensembles

Record Details Similar Records Cite « Share

❌

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Barber, M. J. (2012). Classification ensemble methods for mitigating concept drift within online data streams. (Masters Thesis). Colorado State University. Retrieved from http://hdl.handle.net/10217/67994

Chicago Manual of Style (16^th Edition):

Barber, Michael J. “Classification ensemble methods for mitigating concept drift within online data streams.” 2012. Masters Thesis, Colorado State University. Accessed March 09, 2021. http://hdl.handle.net/10217/67994.

MLA Handbook (7^th Edition):

Barber, Michael J. “Classification ensemble methods for mitigating concept drift within online data streams.” 2012. Web. 09 Mar 2021.

Vancouver:

Barber MJ. Classification ensemble methods for mitigating concept drift within online data streams. [Internet] [Masters thesis]. Colorado State University; 2012. [cited 2021 Mar 09]. Available from: http://hdl.handle.net/10217/67994.

Council of Science Editors:

Barber MJ. Classification ensemble methods for mitigating concept drift within online data streams. [Masters Thesis]. Colorado State University; 2012. Available from: http://hdl.handle.net/10217/67994

Colorado State University

5. Sutton, Andrew M. Analysis of combinatorial search spaces for a class of NP-hard problems, An.

Degree: PhD, Computer Science, 2011, Colorado State University

URL: http://hdl.handle.net/10217/50161

► Given a finite but very large set of states X and a real-valued objective function ƒ defined on X, combinatorial optimization refers to the problem… (more)

▼ Given a finite but very large set of states X and a real-valued objective function ƒ defined on X, combinatorial optimization refers to the problem of finding elements of X that maximize (or minimize) ƒ. Many combinatorial search algorithms employ some perturbation operator to hill-climb in the search space. Such perturbative local search algorithms are state of the art for many classes of NP-hard combinatorial optimization problems such as maximum k-satisfiability, scheduling, and problems of graph theory. In this thesis we analyze combinatorial search spaces by expanding the objective function into a (sparse) series of basis functions. While most analyses of the distribution of function values in the search space must rely on empirical sampling, the basis function expansion allows us to directly study the distribution of function values across regions of states for combinatorial problems without the need for sampling. We concentrate on objective functions that can be expressed as bounded pseudo-Boolean functions which are NP-hard to solve in general. We use the basis expansion to construct a polynomial-time algorithm for exactly computing constant-degree moments of the objective function ƒ over arbitrarily large regions of the search space. On functions with restricted codomains, these moments are related to the true distribution by a system of linear equations. Given low moments supplied by our algorithm, we construct bounds of the true distribution of ƒ over regions of the space using a linear programming approach. A straightforward relaxation allows us to efficiently approximate the distribution and hence quickly estimate the count of states in a given region that have certain values under the objective function. The analysis is also useful for characterizing properties of specific combinatorial problems. For instance, by connecting search space analysis to the theory of inapproximability, we prove that the bound specified by Grover's maximum principle for the Max-Ek-Lin-2 problem is sharp. Moreover, we use the framework to prove certain configurations are forbidden in regions of the Max-3-Sat search space, supplying the first theoretical confirmation of empirical results by others. Finally, we show that theoretical results can be used to drive the design of algorithms in a principled manner by using the search space analysis developed in this thesis in algorithmic applications. First, information obtained from our moment retrieving algorithm can be used to direct a hill-climbing search across plateaus in the Max-k-Sat search space. Second, the analysis can be used to control the mutation rate on a (1+1) evolutionary algorithm on bounded pseudo-Boolean functions so that the offspring of each search point is maximized in expectation. For these applications, knowledge of the search space structure supplied by the analysis translates to significant gains in the performance of search. Advisors/Committee Members: Whitley, L. Darrell (advisor), Howe, Adele E. (advisor), Chong, Edwin K. P. (committee member), Bohm, A. P. Willem (committee member).

Subjects/Keywords: combinatorial optimization; combinatorial search; local search; pseudo-Boolean functions; search space analysis

Record Details Similar Records Cite « Share

❌

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Sutton, A. M. (2011). Analysis of combinatorial search spaces for a class of NP-hard problems, An. (Doctoral Dissertation). Colorado State University. Retrieved from http://hdl.handle.net/10217/50161

Chicago Manual of Style (16^th Edition):

Sutton, Andrew M. “Analysis of combinatorial search spaces for a class of NP-hard problems, An.” 2011. Doctoral Dissertation, Colorado State University. Accessed March 09, 2021. http://hdl.handle.net/10217/50161.

MLA Handbook (7^th Edition):

Sutton, Andrew M. “Analysis of combinatorial search spaces for a class of NP-hard problems, An.” 2011. Web. 09 Mar 2021.

Vancouver:

Sutton AM. Analysis of combinatorial search spaces for a class of NP-hard problems, An. [Internet] [Doctoral dissertation]. Colorado State University; 2011. [cited 2021 Mar 09]. Available from: http://hdl.handle.net/10217/50161.

Council of Science Editors:

Sutton AM. Analysis of combinatorial search spaces for a class of NP-hard problems, An. [Doctoral Dissertation]. Colorado State University; 2011. Available from: http://hdl.handle.net/10217/50161

Colorado State University

6. Sojda, Richard S. Artificial intelligence based decision support for trumpeter swan management.

Degree: PhD, Forest Sciences, 2002, Colorado State University

URL: http://hdl.handle.net/10217/954

► The number of trumpeter swans (Cygnus buccinator) breeding in the Tri-State area where Montana, Idaho, and Wyoming come together has declined to just a few… (more)

▼ The number of trumpeter swans (Cygnus buccinator) breeding in the Tri-State area where Montana, Idaho, and Wyoming come together has declined to just a few hundred pairs. However, these birds are part of the Rocky Mountain Population which additionally has over 3,500 birds breeding in Alberta, British Columbia, Northwest Territories, and Yukon Territory. To a large degree, these birds seem to have abandoned traditional migratory pathways in the flyway. Waterfowl managers have been interested in decision support tools that would help them explore simulated management scenarios in their quest towards reaching population recovery and the reestablishment of traditional migratory pathways. I have developed a decision support system to assist biologists with such management, especially related to wetland ecology. Decision support systems use a combination of models, analytical techniques, and information retrieval to help develop and evaluate appropriate alternatives. Swan management is a domain that is ecologically complex, and this complexity is compounded by spatial and temporal issues. The Distributed Environment Centered Agent Framework (DECAF) was successful at integrating communications among agents, integrating ecological knowledge, and simulating swan distributions through implementation of a queuing system. The work I have conducted indicates a need for determining what other factors might allow a deeper understanding of the effects of management actions on the flyway distribution of waterfowl. Knowing those would allow the more refined development of algorithms for effective decision support systems via collaboration by intelligent agents. Additional, specific conclusions and ideas for future research related both to waterfowl ecology and to the use of multiagent systems have been triggered by the validation work. Advisors/Committee Members: Dean, Denis J. (advisor), Fredrickson, Leigh H. (committee member), Howe, Adele E. (committee member), Loomis, John B. (committee member).

Subjects/Keywords: waterfowl ecology; Rocky Mountain trumpeter swans; ecological decision support systems; swan management; distributed environment centered agent framework; (DECAF); Trumpeter swan; Waterfowl management; Decision support systems; Cygnus buccinator

Record Details Similar Records Cite « Share

❌

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Sojda, R. S. (2002). Artificial intelligence based decision support for trumpeter swan management. (Doctoral Dissertation). Colorado State University. Retrieved from http://hdl.handle.net/10217/954

Chicago Manual of Style (16^th Edition):

Sojda, Richard S. “Artificial intelligence based decision support for trumpeter swan management.” 2002. Doctoral Dissertation, Colorado State University. Accessed March 09, 2021. http://hdl.handle.net/10217/954.

MLA Handbook (7^th Edition):

Sojda, Richard S. “Artificial intelligence based decision support for trumpeter swan management.” 2002. Web. 09 Mar 2021.

Vancouver:

Sojda RS. Artificial intelligence based decision support for trumpeter swan management. [Internet] [Doctoral dissertation]. Colorado State University; 2002. [cited 2021 Mar 09]. Available from: http://hdl.handle.net/10217/954.

Council of Science Editors:

Sojda RS. Artificial intelligence based decision support for trumpeter swan management. [Doctoral Dissertation]. Colorado State University; 2002. Available from: http://hdl.handle.net/10217/954

Colorado State University

7. Watson, Jean-Paul. Empirical modeling and analysis of local search algorithms for the job-shop scheduling problem.

Degree: PhD, Computer Science, 2003, Colorado State University

URL: http://hdl.handle.net/10217/26811

► Local search algorithms are among the most effective approaches for locating high-quality solutions to a wide range of combinatorial optimization problems. However, our theoretical understanding… (more)

▼ Local search algorithms are among the most effective approaches for locating high-quality solutions to a wide range of combinatorial optimization problems. However, our theoretical understanding of these algorithms is very limited, leading to significant problems for both researchers and practitioners. Specifically, the lack of a theory of local search impedes the development of more effective algorithms, prevents practitioners from identifying the algorithm most appropriate for a given problem, and allows widespread conjecture and misinformation regarding the benefits and/or drawbacks of particular algorithms. This thesis represents a significant step toward a theory of local search. Using empirical methods, we develop theoretical models of the behavior of four well-known local search algorithms: a random walk, tabu search, iterated local search, and simulated annealing. The analysis proceeds in the context of the well-known job-shop scheduling problem, one of the most difficult NP-hard problems encountered in practice. The large volume of prior research on the job-shop scheduling problem provides a diverse range of available algorithms and problem instances, in addition to numerous empirical observations regarding local search algorithm behavior; the latter are used to validate our behavioral models. We show that all four local search algorithms can be modeled with high fidelity using straightforward variations of a generalized one-dimensional Markov chain. The states in these models represent sets of solutions a given fixed distance from the nearest optimal solution. The transition probabilities in all of the models are remarkably similar, in that search is consistently biased toward solutions that are roughly equidistant from the nearest optimal solution and solutions that are maximally distant from the nearest optimal solution. Surprisingly, the qualitative form of the transition probabilities is simply due to the structure of the representation used to encode solutions: the binary hypercube. The models account for between 96% and 99% of the variability in the cost required to locate both optimal and sub-optimal solutions to a wide range of problem instances, and provide explanations for numerous phenomena related to problem difficulty for local search in the job-shop scheduling problem. In the course of our analysis, we also disprove many conjectures regarding the behavior and benefits of particular algorithms. Our research indicates that despite their effectiveness, local search algorithms for the job-shop scheduling problem exhibit surprisingly simple run-time dynamics. Further, we observe minimal differences between the dynamical behavior of different algorithms. As expected given similar run-time dynamics, although contrary to numerous reports appearing in the literature, we also show that the performance of different algorithms is largely indistinguishable. Ultimately, our behavioral models serve to unify and provide explanations for a large body of observations regarding problem difficulty for local… Advisors/Committee Members: Howe, Adele E. (advisor), Whitley, L. Darrell (advisor), Chong, Edwin Kah Pin (committee member), Bohm, Anton Willem (committee member).

Subjects/Keywords: random walk; tabu search; job-shop scheduling problem; empirical methods; local search algorithms; iterated local search; simulated annealing; Production scheduling – Computer simulation

Record Details Similar Records Cite « Share

❌

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Watson, J. (2003). Empirical modeling and analysis of local search algorithms for the job-shop scheduling problem. (Doctoral Dissertation). Colorado State University. Retrieved from http://hdl.handle.net/10217/26811

Chicago Manual of Style (16^th Edition):

Watson, Jean-Paul. “Empirical modeling and analysis of local search algorithms for the job-shop scheduling problem.” 2003. Doctoral Dissertation, Colorado State University. Accessed March 09, 2021. http://hdl.handle.net/10217/26811.

MLA Handbook (7^th Edition):

Watson, Jean-Paul. “Empirical modeling and analysis of local search algorithms for the job-shop scheduling problem.” 2003. Web. 09 Mar 2021.

Vancouver:

Watson J. Empirical modeling and analysis of local search algorithms for the job-shop scheduling problem. [Internet] [Doctoral dissertation]. Colorado State University; 2003. [cited 2021 Mar 09]. Available from: http://hdl.handle.net/10217/26811.

Council of Science Editors:

Watson J. Empirical modeling and analysis of local search algorithms for the job-shop scheduling problem. [Doctoral Dissertation]. Colorado State University; 2003. Available from: http://hdl.handle.net/10217/26811

Colorado State University

8. Kretchmar, R. Matthew. Synthesis of reinforcement learning and robust control theory, A.

Degree: PhD, Computer Science, 2000, Colorado State University

URL: http://hdl.handle.net/10217/26305

► The pursuit of control algorithms with improved performance drives the entire control research community as well as large parts of the mathematics, engineering, and artificial… (more)

▼ The pursuit of control algorithms with improved performance drives the entire control research community as well as large parts of the mathematics, engineering, and artificial intelligence research communities. A fundamental limitation on achieving control performance is the conflicting requirement of maintaining system stability. In general, the more aggressive is the controller, the better the control performance but also the closer to system instability. Robust control is a collection of theories, techniques, the tools that form one of the leading edge approaches to control. Most controllers are designed not on the physical plant to be controlled, but on a mathematical model of the plant; hence, these controllers often do not perform well on the physical plant and are sometimes unstable. Robust control overcomes this problem by adding uncertainty to the mathematical model. The result is a more general, less aggressive controller which performs well on the both the model and the physical plant. However, the robust control method also sacrifices some control performance in order to achieve its guarantees of stability. Reinforcement learning based neural networks offer some distinct advantages for improving control performance. Their nonlinearity enables the neural network to implement a wider range of control functions, and their adaptability permits them to improve control performance via on-line, trial-and-error learning. However, neuro-control is typically plagued by a lack of stability guarantees. Even momentary instability cannot be tolerated in most physical plants, and thus, the threat of instability prohibits the application of neuro-control in many situations. In this dissertation, we develop a stable neuro-control scheme by synthesizing the two fields of reinforcement learning and robust control theory. We provide a learning system with many of the advantages of neuro-control. Using functional uncertainty to represent the nonlinear and time-varying components of the neuro networks, we apply the robust control techniques to guarantee the stability of our neuro-controller. Our scheme provides stable control not only for a specific fixed-weight, neural network, but also for a neuro-controller in which the weights are changing during learning. Furthermore, we apply our stable neuro-controller to several control tasks to demonstrate that the theoretical stability guarantee is readily applicable to real-life control situations. We also discuss several problems we encounter and identify potential avenues of future research. Advisors/Committee Members: Anderson, Charles (advisor), Howe, Adele E. (committee member), Whitley, L. Darrell (committee member), Young, Peter M. (committee member), Hittle, Douglas C. (committee member).

Subjects/Keywords: robust control theory; neuro-control scheme; neuro-controller; neural networks; Reinforcement learning; Control theory

Record Details Similar Records Cite « Share

❌

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Kretchmar, R. M. (2000). Synthesis of reinforcement learning and robust control theory, A. (Doctoral Dissertation). Colorado State University. Retrieved from http://hdl.handle.net/10217/26305

Chicago Manual of Style (16^th Edition):

Kretchmar, R Matthew. “Synthesis of reinforcement learning and robust control theory, A.” 2000. Doctoral Dissertation, Colorado State University. Accessed March 09, 2021. http://hdl.handle.net/10217/26305.

MLA Handbook (7^th Edition):

Kretchmar, R Matthew. “Synthesis of reinforcement learning and robust control theory, A.” 2000. Web. 09 Mar 2021.

Vancouver:

Kretchmar RM. Synthesis of reinforcement learning and robust control theory, A. [Internet] [Doctoral dissertation]. Colorado State University; 2000. [cited 2021 Mar 09]. Available from: http://hdl.handle.net/10217/26305.

Council of Science Editors:

Kretchmar RM. Synthesis of reinforcement learning and robust control theory, A. [Doctoral Dissertation]. Colorado State University; 2000. Available from: http://hdl.handle.net/10217/26305

		in
And / Or
		in
And / Or
		in
And / Or
		in

Open Access Theses and Dissertations