Satya Gautam Vadlamudi

Anytime column search

Anytime heuristic search algorithms are widely applied where best-first search algorithms such as A* require large or often unacceptable amounts of time and memory. Anytime algorithms produce a solution quickly and iteratively improve the solution quality. In this paper, we propose novel anytime heuristic search algorithms with a common underlying strategy called Column Search. The proposed algorithms are complete and guarantee to produce an optimal solution. Experimental results on sliding-tile puzzle problem, traveling salesman problem, and robotic arm trajectory planning problem show the efficacy of proposed methods compared to state-of-the-art anytime heuristic search algorithms.

MAWA∗-a memory-bounded anytime heuristic-search algorithm.

This paper presents a heuristic-search algorithm called Memory-bounded Anytime Window A*(MAWA*), which is complete, anytime, and memory bounded. MAWA* uses the window-bounded anytime-search methodology of AWA* as the basic framework and combines it with the memory-bounded A* -like approach to handle restricted memory situations. Simple and efficient versions of MAWA* targeted for tree search have also been presented. Experimental results of the sliding-tile puzzle problem and the traveling-salesman problem show the significant advantages of the proposed algorithm over existing methods.

Robustness Analysis of Embedded Control Systems with Respect to Signal Perturbations: Finding Minimal Counterexamples Using Fault Injection

Fault-tolerance of embedded control systems is of great importance, given their wide usage in various domains such as aeronautics, automotive, medical, and so on. Signal perturbations such as small amounts of noise, shift, and spikes, can sometimes severely hamper the performance of the system, apart from complete failure of components and links. Finding minimal counterexamples (perturbations on the system leading to violation of fault-tolerance requirements) can be of great assistance to control system designers in understanding and adjusting the fault-tolerance behavior of the system. Fault injection is an effective method for dependability analysis of such systems. In this paper, we introduce the concept of dominating sets of perturbations, and define a minimal set of counterexamples called the basis. We propose effective methods using a simulation-based fault injection technique on Simulink models for finding the basis set at an early stage of design, given the fault specification and fault-tolerance requirements. Experimental results on two different control system examples from the Simulink automotive library demonstrate the efficacy of the proposed framework.

Formal Methods for Early Analysis of Functional Reliability in Component-Based Embedded Applications

We present formal methods for determining whether a set of components with given reliability certificates for specific functional properties are adequate to guarantee desired end-to-end properties with specified reliability requirements. We introduce a formal notion for the reliability gap in component-based designs and demonstrate the proposed approach for analyzing this gap using a case study developed around an Elevator Control System.

Incremental Beam search

Beam search is a heuristic search algorithm that explores a state-space graph by expanding w most promising nodes at each level (depth) of the graph, where w is called the beam-width which is taken as input from the user. The quality of the solution produced by beam search does not always monotonically improve with the increase in beam-width making it difficult to choose an appropriate beam-width for effective use. We present an algorithm called Incremental Beam Search (IncB) which guarantees monotonicity, and is also anytime in nature. Experimental results on the sliding-tile puzzle, the traveling salesman, and the single-machine scheduling problems show that IncB significantly outperforms basic monotonic methods such as iterative widening beam search as well as some of the state-of-the-art anytime heuristic search algorithms in terms of the quality of the solution produced at the end as well as the anytime performance.

Anytime Pack Heuristic Search

Heuristic search is a fundamental problem solving technique in artificial intelligence. In this paper, we propose an anytime heuristic search algorithm called Anytime Pack Search (APS) which helps in solving hard combinatorial search problems efficiently. It expands nodes of a search graph in a localized best-first manner so as to converge towards good quality solutions at regular intervals. APS is complete on bounded graphs and guarantees termination with an optimal solution. Experimental results on the sliding-tile puzzle problem, the traveling salesman problem, and the single-machine scheduling problem show that APS significantly outperforms some of the state-of-the-art anytime algorithms.

A framework for early stage quality-fault tolerance analysis of embedded control systems

This work presents a static-analysis based method for analyzing the robustness of a given embedded control system design, in the presence of quality-faults in sensors, software components, and inter-connections. The method characterizes the individual components of the system by storing the relations between the precision of inputs and the precision of outputs in what we call, lookup tables (LUTs). A network of LUTs thus formed which represent the given control system is converted into a satisfiability modulo theory (SMT) instance, such that a satisfying assignment corresponds to a potential counterexample (the set of quality-faults which violate the given fault-tolerance requirements) or hot-spot in the design. Hot-spots obtained in this manner are counter-verified through simulation to filter the false-positives. Experimental results on the fault-tolerant fuel controller from Simulink automotive library demonstrate the efficacy of the proposed approach.

A Mobility Simulation Framework Of Humans With Group Behavior Modeling

We present a mobility simulation framework that simulates the movement behaviors of people to generate spatiotemporal movement data. There is a growing interest in applications that make use of patterns mined from spatio-temporal data. However, since the availability of actual spatio-temporal movement data in the public domain is limited, it is useful to have simulation frameworks that generate data close to the real life behavior of people, so that data mining techniques can be tested. We argue that modeling group behavior effectively is a key element of any real-life simulation framework, because there are many applications that require the knowledge of groups and events. In this work, we propose generic models to represent individual and group movement behaviors. We present an algorithm that takes various behaviors created using the proposed models, and generates spatio-temporal movement data for as many individuals as needed. Experimental analysis shows the efficacy of the proposed framework handling a broad spectrum of behaviors with high scalability.

Finding Critical Components in Embedded Control Systems Sensitive to Quality-Faults

Embedded control systems used in safety critical systems need to be robust to quality-faults such as shift, noise, and spikes. Methods for finding counterexamples (quality-faults whose injection leads to violation of fault-tolerance requirements) at an early stage of control system design were proposed in the literature. Given these counterexamples, control design should be improved such that it is quality-fault tolerant. In this paper, we propose an effective methodology for finding critical components in embedded control systems which are sensitive to quality-faults based on the given counterexamples, which is an important step towards improving the control design. Experimental results on the fault-tolerant fuel controller of Simulink library show the efficacy of proposed methodology.

—A Memory-Bounded Anytime Heuristic-Search Algorithm

This paper presents a heuristic-search algorithm called Memory-bounded Anytime Window A*(MAWA*), which is complete, anytime, and memory bounded. MAWA* uses the window-bounded anytime-search methodology of AWA* as the basic framework and combines it with the memory-bounded A* -like approach to handle restricted memory situations. Simple and efficient versions of MAWA* targeted for tree search have also been presented. Experimental results of the sliding-tile puzzle problem and the traveling-salesman problem show the significant advantages of the proposed algorithm over existing methods.