arXiv subjects

Kamalakar Karlapalem

Publications and source records attributed to Kamalakar Karlapalem.

25 records · Page 2Linked to original sources

arXiv2016

Wheeled Robots playing Chain Catch: Strategies and Evaluation

Robots playing games that humans are adept in is a challenge. We studied robotic agents playing Chain Catch game as a Multi-Agent System (MAS). Our game starts with a traditional Catch game similar to Pursuit evasion, and further extends it to form a growing chain of predator agents to chase remaining preys. Hence Chain Catch is a combination of two challenges - pursuit domain and robotic chain formation. These are games that require team of robotic agents to cooperate among themselves and to compete with other group of agents through quick decision making. In this paper, we present a Chain Catch simulator that allows us to incorporate game rules, design strategies and simulate the game play. We developed cost model driven strategies for each of Escapee, Catcher and Chain. Our results show that Sliding slope strategy is the best strategy for Escapees whereas Tagging method is the best method for chain s movement in Chain Catch. We also use production quality robots to implement the game play in a physical environment and analyze game strategies on real robots. Our real robots implementation in different scenarios shows that game strategies work as expected and a complete chain formation takes place successfully in each game.

cs.MA↗

arXiv2015

Effective Handling of Urgent Jobs - Speed Up Scheduling for Computing Applications

A queue is required when a service provider is not able to handle jobs arriving over the time. In a highly flexible and dynamic environment, some jobs might demand for faster execution at run-time especially when the resources are limited and the jobs are competing for acquiring resources. A user might demand for speed up (reduced wait time) for some of the jobs present in the queue at run time. In such cases, it is required to accelerate (directly sending the job to the server) urgent jobs (requesting for speed up) ahead of other jobs present in the queue for an earlier completion of urgent jobs. Under the assumption of no additional resources, such acceleration of jobs would result in slowing down of other jobs present in the queue. In this paper, we formulate the problem of Speed Up Scheduling without acquiring any additional resources for the scheduling of on-line speed up requests posed by a user at run-time and present algorithms for the same. We apply the idea of Speed Up Scheduling to two different domains -Web Scheduling and CPU Scheduling. We demonstrate our results with a simulation based model using trace driven workload and synthetic datasets to show the usefulness of Speed Up scheduling. Speed Up provides a new way of addressing urgent jobs, provides a different evaluation criteria for comparing scheduling algorithms and has practical applications.

cs.PF↗

arXiv2015

Exploiting Near Time Forecasting From Social Network To Decongest Traffic

Preventing traffic congestion by forecasting near time traffic flows is an important problem as it leads to effective use of transport resources. Social network provides information about activities of humans and social events. Thus, with the help of social network, we can extract which humans will attend a particular event (in near time) and can estimate flow of traffic based on it. This opens up a wide area of research which poses need to have a framework for traffic management that can capture essential parameters of real-life behaviour and provide a way to iterate upon and evaluate new ideas. In this paper, we present building blocks of a framework and a system to simulate a city with its transport system, humans and their social network. We emphasize on relevant parameters selected and modular design of the framework. Our framework defines metrics to evaluate congestion avoidance strategies. To show utility of the framework, we present experimental studies of few strategies on a public transport system.

cs.MA↗

arXiv2015

Crowd Congestion and Stampede Management through Multi Robotic Agents

Crowd management is a complex, challenging and crucial task. Lack of appropriate management of crowd has, in past, led to many unfortunate stampedes with significant loss of life. To increase the crowd management efficiency, we deploy automated real time detection of stampede prone areas. Then, we use robotic agents to aid the crowd management police in controlling the crowd in these stampede prone areas. While doing so, we aim for minimum interference by robotic agents in our environment. Thereby not disturbing the ambiance and aesthetics of the place. We evaluate the effectiveness of our model in dealing with difficult scenarios like emergency evacuation and presence of localized congestion. Lastly, we simulate a multi agent system based on our model and use it to illustrate the utility of robotic agents for detecting and reducing congestion.

cs.MA↗

arXiv2014

Inequity aversion and the evolution of cooperation

Evolution of cooperation is a widely studied problem in biology, social science, economics, and artificial intelligence. Most of the existing approaches that explain cooperation rely on some notion of direct or indirect reciprocity. These reciprocity based models assume agents recognize their partner and know their previous interactions, which requires advanced cognitive abilities. In this paper we are interested in developing a model that produces cooperation without requiring any explicit memory of previous game plays. Our model is based on the notion of, a concept introduced within behavioral economics, whereby individuals care about payoff equality in outcomes. Here we explore the effect of using income inequality to guide partner selection and interaction. We study our model by considering both the well-mixed and the spatially structured population and present the conditions under which cooperation becomes dominant. Our results support the hypothesis that inequity aversion promotes cooperative relationship among nonkin.

cs.GT↗

arXiv2004

Subset Queries in Relational Databases

In this paper, we motivated the need for relational database systems to support subset query processing. We defined new operators in relational algebra, and new constructs in SQL for expressing subset queries. We also illustrated the applicability of subset queries through different examples expressed using extended SQL statements and relational algebra expressions. Our aim is to show the utility of subset queries for next generation applications.

cs.DB↗

arXiv2002

Sprinkling Selections over Join DAGs for Efficient Query Optimization

In optimizing queries, solutions based on AND/OR DAG can generate all possible join orderings and select placements before searching for optimal query execution strategy. But as the number of joins and selection conditions increase, the space and time complexity to generate optimal query plan increases exponentially. In this paper, we use join graph for a relational database schema to either pre-compute all possible join orderings that can be executed and store it as a join DAG or, extract joins in the queries to incrementally build a history join DAG as and when the queries are executed. The select conditions in the queries are appropriately placed in the retrieved join DAG (or, history join DAG) to generate optimal query execution strategy. We experimentally evaluate our query optimization technique on TPC-D/H query sets to show their effectiveness over AND/OR DAG query optimization strategy. Finally, we illustrate how our technique can be used for efficient multiple query optimization and selection of materialized views in data warehousing environments.

cs.DB↗