arXiv subjects

Shrisha Rao

Publications and source records attributed to Shrisha Rao.

At least 37 records · Page 2Linked to original sources

arXiv2021

Interleaving Fast and Slow Decision Making

The "Thinking, Fast and Slow" paradigm of Kahneman proposes that we use two different styles of thinking -- a fast and intuitive System 1 for certain tasks, along with a slower but more analytical System 2 for others. While the idea of using this two-system style of thinking is gaining popularity in AI and robotics, our work considers how to interleave the two styles of decision-making, i.e., how System 1 and System 2 should be used together. For this, we propose a novel and general framework which includes a new System 0 to oversee Systems 1 and 2. At every point when a decision needs to be made, System 0 evaluates the situation and quickly hands over the decision-making process to either System 1 or System 2. We evaluate such a framework on a modified version of the classic Pac-Man game, with an already-trained RL algorithm for System 1, a Monte-Carlo tree search for System 2, and several different possible strategies for System 0. As expected, arbitrary switches between Systems 1 and 2 do not work, but certain strategies do well. With System 0, an agent is able to perform better than one that uses only System 1 or System 2.

cs.AI↗

arXiv2020

Floating-Point Multiplication Using Neuromorphic Computing

Neuromorphic computing describes the use of VLSI systems to mimic neuro-biological architectures and is also looked at as a promising alternative to the traditional von Neumann architecture. Any new computing architecture would need a system that can perform floating-point arithmetic. In this paper, we describe a neuromorphic system that performs IEEE 754-compliant floating-point multiplication. The complex process of multiplication is divided into smaller sub-tasks performed by components Exponent Adder, Bias Subtractor, Mantissa Multiplier and Sign OF/UF. We study the effect of the number of neurons per bit on accuracy and bit error rate, and estimate the optimal number of neurons needed for each component.

cs.ET↗

arXiv2019

Egocentric Bias and Doubt in Cognitive Agents

Modeling social interactions based on individual behavior has always been an area of interest, but prior literature generally presumes rational behavior. Thus, such models may miss out on capturing the effects of biases humans are susceptible to. This work presents a method to model egocentric bias, the real-life tendency to emphasize one's own opinion heavily when presented with multiple opinions. We use a symmetric distribution centered at an agent's own opinion, as opposed to the Bounded Confidence (BC) model used in prior work. We consider a game of iterated interactions where an agent cooperates based on its opinion about an opponent. Our model also includes the concept of domain-based self-doubt, which varies as the interaction succeeds or not. An increase in doubt makes an agent reduce its egocentricity in subsequent interactions, thus enabling the agent to learn reactively. The agent system is modeled with factions not having a single leader, to overcome some of the issues associated with leader-follower factions. We find that agents belonging to factions perform better than individual agents. We observe that an intermediate level of egocentricity helps the agent perform at its best, which concurs with conventional wisdom that neither overconfidence nor low self-esteem brings benefits.

cs.AI↗

arXiv2019

Scheduling Under Power and Energy Constraints

Given a system model where machines have distinct speeds and power ratings but are otherwise compatible, we consider various problems of scheduling under resource constraints on the system which place the restriction that not all machines can be run at once. These can be power, energy, or makespan constraints on the system. Given such constraints, there are problems with divisible as well as non-divisible jobs. In the setting where there is a constraint on power, we show that the problem of minimizing makespan for a set of divisible jobs is NP-hard by reduction to the knapsack problem. We then show that scheduling to minimize energy with power constraints is also NP-hard. We then consider scheduling with energy and makespan constraints with divisible jobs and show that these can be solved in polynomial time, and the problems with non-divisible jobs are NP-hard. We give exact and approximation algorithms for these problems as required.

cs.DS↗

arXiv2018

Distributed and Efficient Resource Balancing Among Many Suppliers and Consumers

Achieving a balance of supply and demand in a multi-agent system with many individual self-interested and rational agents that act as suppliers and consumers is a natural problem in a variety of real-life domains---smart power grids, data centers, and others. In this paper, we address the profit-maximization problem for a group of distributed supplier and consumer agents, with no inter-agent communication. We simulate a scenario of a market with $S$ suppliers and $C$ consumers such that at every instant, each supplier agent supplies a certain quantity and simultaneously, each consumer agent consumes a certain quantity. The information about the total amount supplied and consumed is only kept with the center. The proposed algorithm is a combination of the classical additive-increase multiplicative-decrease (AIMD) algorithm in conjunction with a probabilistic rule for the agents to respond to a capacity signal. This leads to a nonhomogeneous Markov chain and we show almost sure convergence of this chain to the social optimum, for our market of distributed supplier and consumer agents. Employing this AIMD-type algorithm, the center sends a feedback message to the agents in the supplier side if there is a scenario of excess supply, or to the consumer agents if there is excess consumption. Each agent has a concave utility function whose derivative tends to 0 when an optimum quantity is supplied/consumed. Hence when social convergence is reached, each agent supplies or consumes a quantity which leads to its individual maximum profit, without the need of any communication. So eventually, each agent supplies or consumes a quantity which leads to its individual maximum profit, without communicating with any other agents. Our simulations show the efficacy of this approach.

cs.MA↗

arXiv2018

Energy-Efficient Scheduling: Classification, Bounds, and Algorithms

The problem of attaining energy efficiency in distributed systems is of importance, but a general, non-domain-specific theory of energy-minimal scheduling is far from developed. In this paper, we classify the problems of energy-minimal scheduling and present theoretical foundations of the same. We derive results concerning energy-minimal scheduling of independent jobs in a distributed system with functionally similar machines with different working and idle power ratings. The machines considered in our system can have identical as well as different speeds. If the jobs can be divided into arbitrary parts, we show that the minimum-energy schedule can be generated in linear time and give exact scheduling algorithms. For the cases where jobs are non-divisible, we prove that the scheduling problems are NP-hard and also give approximation algorithms for the same along with their bounds.

cs.DC↗

arXiv2018

Distributed Approximation Algorithms for the Combinatorial Motion Planning Problem

We present a new $4$-approximation algorithm for the Combinatorial Motion Planning problem which runs in $\mathcal{O}(n^2α(n^2,n))$ time, where $α$ is the functional inverse of the Ackermann function, and a fully distributed version for the same in asynchronous message passing systems, which runs in $\mathcal{O}(n\log_2n)$ time with a message complexity of $\mathcal{O}(n^2)$. This also includes the first fully distributed algorithm in asynchronous message passing systems to perform "shortcut" operations on paths, a procedure which is important in approximation algorithms for the vehicle routing problem and its variants. We also show that our algorithm gives feasible solutions to the $k$-TSP problem with an approximation factor of $2$ in both centralized and distributed environments. The broad idea of the algorithm is to distribute the set of vertices into two subsets and construct paths for each salesman over each of the two subsets. Finally we combine these pairwise disjoint paths for each salesman to obtain a set of paths that span the entire graph. This is similar to the algorithm by Yadlapalli et. al. \cite{3.66} but differs in respect to the fact that it does not require us to use minimum cost matching as a subroutine, and hence can be easily distributed.

cs.DM↗

arXiv2017

Distributed Approximation Algorithms for the Multiple Knapsack Problem

We consider the distributed version of the Multiple Knapsack Problem (MKP), where $m$ items are to be distributed amongst $n$ processors, each with a knapsack. We propose different distributed approximation algorithms with a tradeoff between time and message complexities. The algorithms are based on the greedy approach of assigning the best item to the knapsack with the largest capacity. These algorithms obtain a solution with a bound of $\frac{1}{n+1}$ times the optimum solution, with either $\mathcal{O}\left(m\log n\right)$ time and $\mathcal{O}\left(m n\right)$ messages, or $\mathcal{O}\left(m\right)$ time and $\mathcal{O}\left(mn^{2}\right)$ messages.

cs.DS↗

arXiv2016

A Distance Function for Comparing Straight-Edge Geometric Figures

This paper defines a distance function that measures the dissimilarity between planar geometric figures formed with straight lines. This function can in turn be used in partial matching of different geometric figures. For a given pair of geometric figures that are graphically isomorphic, one function measures the angular dissimilarity and another function measures the edge length disproportionality. The distance function is then defined as the convex sum of these two functions. The novelty of the presented function is that it satisfies all properties of a distance function and the computation of the same is done by projecting appropriate features to a cartesian plane. To compute the deviation from the angular similarity property, the Euclidean distance between the given angular pairs and the corresponding points on the $y=x$ line is measured. Further while computing the deviation from the edge length proportionality property, the best fit line, for the set of edge lengths, which passes through the origin is found, and the Euclidean distance between the given edge length pairs and the corresponding point on a $y=mx$ line is calculated. Iterative Proportional Fitting Procedure (IPFP) is used to find this best fit line. We demonstrate the behavior of the defined function for some sample pairs of figures.

cs.CV↗

arXiv2016

Semiring Properties of Heyting Algebras

The relationship between Heyting algebras (HA) and semirings is explored. A new class of HAs called Symmetric Heyting algebras (SHAs) is proposed, and a necessary condition on SHAs to be consider semirings is given. We define a new mathematical family called Heyting structures, which are similar to semirings, but with Heyting-algebra operators in place of the usual arithmetic operators usually seen in semirings. The impact of the zero-sum free property of semirings on Heyting structures is shown as also the condition under which it is possible to extend one Heyting structure to another. It is also shown that the union of two or more sets forming Heyting structures is again a Heyting structure, if the operators on the new structure are suitably derived from those of the component structures. The analysis also provides a sufficient condition such that the larger Heyting structure satisfying a monotony law implies that the ones forming the union do so as well.

math.RA↗

arXiv2016

Network Flows Under Thermal Restrictions

We define a \emph{thermal network}, which is a network where the flow functionality of a node depends upon its temperature. This model is inspired by several types of real-life networks, and generalizes some conventional network models wherein nodes have fixed capacities and the problem is to maximize the flow through the network. In a thermal network, the temperature of a node increases as traffic moves through it, and nodes may also cool spontaneously over time, or by employing cooling packets. We analyze the problems of maximizing the flow from a source to a sink for both these cases, for a holistic view with respect to the single-source-single-sink dynamic flow problem in a thermal network. We have studied certain properties such a thermal network exhibits, and give closed-form solutions for the maximum flow that can be achieved through such a network.

cs.DM↗

arXiv2014

Degradation Analysis of Probabilistic Parallel Choice Systems

Degradation analysis is used to analyze the useful lifetimes of systems, their failure rates, and various other system parameters like mean time to failure (MTTF), mean time between failures (MTBF), and the system failure rate (SFR). In many systems, certain possible parallel paths of execution that have greater chances of success are preferred over others. Thus we introduce here the concept of probabilistic parallel choice. We use binary and $n$-ary probabilistic choice operators in describing the selections of parallel paths. These binary and $n$-ary probabilistic choice operators are considered so as to represent the complete system (described as a series-parallel system) in terms of the probabilities of selection of parallel paths and their relevant parameters. Our approach allows us to derive new and generalized formulae for system parameters like MTTF, MTBF, and SFR. We use a generalized exponential distribution, allowing distinct installation times for individual components, and use this model to derive expressions for such system parameters.

cs.PF↗

arXiv2013

(m,n)-Semirings and a Generalized Fault Tolerance Algebra of Systems

We propose a new class of mathematical structures called (m,n)-semirings} (which generalize the usual semirings), and describe their basic properties. We also define partial ordering, and generalize the concepts of congruence, homomorphism, ideals, etc., for (m,n)-semirings. Following earlier work by Rao, we consider a system as made up of several components whose failures may cause it to fail, and represent the set of systems algebraically as an (m,n)-semiring. Based on the characteristics of these components we present a formalism to compare the fault tolerance behaviour of two systems using our framework of a partially ordered (m,n)-semiring.

math.GM↗

arXiv2010

Failover in cellular automata

A cellular automata (CA) configuration is constructed that exhibits emergent failover. The configuration is based on standard Game of Life rules. Gliders and glider-guns form the core messaging structure in the configuration. The blinker is represented as the basic computational unit, and it is shown how it can be recreated in case of a failure. Stateless failover using primary-backup mechanism is demonstrated. The details of the CA components used in the configuration and its working are described, and a simulation of the complete configuration is also presented.

cs.AI↗

arXiv2010

Fairness in Combinatorial Auctions

The market economy deals with many interacting agents such as buyers and sellers who are autonomous intelligent agents pursuing their own interests. One such multi-agent system (MAS) that plays an important role in auctions is the combinatorial auctioning system (CAS). We use this framework to define our concept of fairness in terms of what we call as "basic fairness" and "extended fairness". The assumptions of quasilinear preferences and dominant strategies are taken into consideration while explaining fairness. We give an algorithm to ensure fairness in a CAS using a Generalized Vickrey Auction (GVA). We use an algorithm of Sandholm to achieve optimality. Basic and extended fairness are then analyzed according to the dominant strategy solution concept.

cs.GT↗

arXiv2010

The Semiring Properties of Fuzzy Algebras

The relationship between fuzzy algebras and semirings is explored with fuzzy algebra operators replacing the arithmetic operators of semirings. A new class of fuzzy structures which are similar to semirings is defined. Results of partial ordering on fuzzy algebras are shown. The zero-sum free property of semirings on fuzzy structures is shown. The semiring property of homomorphism is mapped to fuzzy structures and results like order- and operator-preservation are described.

math.RA↗

arXiv2009

The Potluck Problem

This paper proposes the Potluck Problem as a model for the behavior of independent producers and consumers under standard economic assumptions, as a problem of resource allocation in a multi-agent system in which there is no explicit communication among the agents.

cs.GT↗

arXiv2009

Parallelization of the LBG Vector Quantization Algorithm for Shared Memory Systems

This paper proposes a parallel approach for the Vector Quantization (VQ) problem in image processing. VQ deals with codebook generation from the input training data set and replacement of any arbitrary data with the nearest codevector. Most of the efforts in VQ have been directed towards designing parallel search algorithms for the codebook, and little has hitherto been done in evolving a parallelized procedure to obtain an optimum codebook. This parallel algorithm addresses the problem of designing an optimum codebook using the traditional LBG type of vector quantization algorithm for shared memory systems and for the efficient usage of parallel processors. Using the codebook formed from a training set, any arbitrary input data is replaced with the nearest codevector from the codebook. The effectiveness of the proposed algorithm is indicated.

cs.CV↗