Pragnesh Jay Modi

IJCAI Conference 2007 Conference Paper

• Evan A. Sultanik
• Pragnesh Jay Modi
• William C. Regli

This paper investigates how to represent and solve multiagent task scheduling as a Distributed Constraint Optimization Problem (DCOP). Recently multiagent researchers have adopted the C_TAEMS language as a standard for multiagent task scheduling. We contribute an automated mapping that transforms C_TAEMS into a DCOP. Further, we propose a set of representational compromises for C_TAEMS that allow existing distributed algorithms for DCOP to be immediately brought to bear on C_TAEMS problems. Next, we demonstrate a key advantage of a constraint based representation is the ability to leverage the representation to do efficient solving. We contribute a set of pre-processing algorithms that leverage existing constraint propagation techniques to do variable domain pruning on the DCOP. We show that these algorithms can result in 96% reduction in state space size for a given set of C_TAEMS problems. Finally, we demonstrate up to a 60% increase in the ability to optimally solve C_TAEMS problems in a reasonable amount of time and in a distributed manner as a result of applying our mapping and domain pruning algorithms.

AAMAS Conference 2007 Conference Paper

Disaster Evacuation Support

• Christopher J. Carpenter
• Christopher J. Dugan
• Joseph B. Kopena
• Robert N. Lass
• Gaurav Naik
• Duc N. Nguyen
• Evan Sultanik
• Pragnesh Jay Modi

AAMAS Conference 2006 Conference Paper

Towards a Reference Model for Intelligent Agent Systems

• Pragnesh Jay Modi
• Spiros Mancoridis
• William M. Mongan
• William Regli
• Israel Mayk

AIJ Journal 2005 Journal Article

Adopt: asynchronous distributed constraint optimization with quality guarantees

• Pragnesh Jay Modi
• Wei-Min Shen
• Milind Tambe
• Makoto Yokoo

The Distributed Constraint Optimization Problem (DCOP) is a promising approach for modeling distributed reasoning tasks that arise in multiagent systems. Unfortunately, existing methods for DCOP are not able to provide theoretical guarantees on global solution quality while allowing agents to operate asynchronously. We show how this failure can be remedied by allowing agents to make local decisions based on conservative cost estimates rather than relying on global certainty as previous approaches have done. This novel approach results in a polynomial-space algorithm for DCOP named Adopt that is guaranteed to find the globally optimal solution while allowing agents to execute asynchronously and in parallel. Detailed experimental results show that on benchmark problems Adopt obtains speedups of several orders of magnitude over other approaches. Adopt can also perform bounded-error approximation—it has the ability to quickly find approximate solutions and, unlike heuristic search methods, still maintain a theoretical guarantee on solution quality.

AAMAS Conference 2005 Conference Paper

Bumping Strategies for the Multiagent Agreement Problem

• Pragnesh Jay Modi
• Manuela Veloso

AAMAS Conference 2005 Conference Paper

Impact of Problem Centralization in Distributed Constraint Optimization Algorithms

• John Davin
• Pragnesh Jay Modi

AAAI Conference 2004 System Paper

CMRadar: A Personal Assistant Agent for Calendar Management

• Pragnesh Jay Modi
• Stephen F. Smith

One of the more compelling visions for agents research is the development of “personal assistant agents” that are tasked with making people and organizations more efficient by autonomously handling routine tasks on behalf of their users. Most recently, several researchers including ourselves have embarked on a large research project, called The Radar Project, whose overall goal is to develop a personalized agent that is able to assist its user in a wide range of everyday tasks. Within this larger project, we are concerned with the more focused task of managing a user’s calendar. While isolated aspects of calendar management have been investigated before, in this paper, we present CMRadar, a complete agent with capabilities ranging across the full spectrum of calendar management, from natural language processing of incoming scheduling-related emails, to making autonomous scheduling decisions, to negotiating with other users, to user interfacing and visualization. Although many research issues remain, we believe CMRadar is the first end-to-end agent for automated calendar management.

AAMAS Conference 2003 Conference Paper

An Asynchronous Complete Method for Distributed Constraint Optimization

• Pragnesh Jay Modi
• Wei-Min Shen
• Milind Tambe
• Makoto Yokoo

AAAI Conference 2002 Short Paper

Distributed Constraint Optimization and Its Application to Multiagent Resource Allocation

• Pragnesh Jay Modi

Distributed optimization requires the optimization of a global objective function that is distributed among a set of autonomous, communicating agents and is unknown by any individual agent. The problem is inherently distributed and the solution strategy has no control over a given distribution. Constraint based techniques offer a promising approach for coordinating a set of distributed agents to find optimal solutions. However previous work has the following limitations. First, representation is limited to binary good/nogood constraints. In many domains, it is more natural to represent constraints as having degrees of valuation. Second, previous work on distributed optimization problems has relied on synchronous sequential computation to find optimal solutions. This is slow and requires agents to block for messages. Third, previous asychronous approaches lack any guarantees of optimality even when given sufficient time and provide little guidance on how to trade-off solution quality for time-to-solution when time is limited. This work proposes Adopt, an asynchronous, distributed, complete method for solving distributed optimization problems. The fundamental ideas in Adopt are to represent constraints as discrete functions (or valuations) --- instead of binary good/nogood values --- and to use the evaluation of these constraints to measure progress towards optimality. In addition, Adopt uses a sound and complete partial solution combination method to allow non-sequential, asychronous computation. Finally, Adopt is not only provably optimal when given enough time, but allows solution time/quality tradeoffs when time is limited. Adopt is applied to a real-world distributed resource allocation problem. Distributed resource allocation is a general problem in which a set of agents must optimally assign their resources to a set of tasks with respect to certain criteria. It arises in many real-world domains such as distributed sensor networks, disaster rescue, hospital scheduling, and others.

AAAI Conference 1998 Conference Paper

Modeling Web Sources for Information Integration

• Craig A. Knoblock
• Jose Luis Ambite
• Pragnesh Jay Modi
• Andrew G. Philpot

The Web is based on a browsing paradigm that makes it difficult to retrieve and integrate data from multiple sites. y-k&y, the only w&y t; g & this is to bl_? ild_ specialized applications, which are time-consuming to develop and difficult to maintain. We are addressing this problem by creating the technology and tools for rapidly constructing information agents that extract, query, and integrate data from web sources. Our approach is based on a simple, uniform representation that makes it efficient to integrate multiple sources. Instead of building specialized algorithms for handling web sources, we have developed methods for mapping web sources into this uniform representation. This approach builds on work from knowledge representation, machine learning and automated planning. The re- =nltims amtom rxllprl Arinclnp rnak~. c it, fast, a. nd chc3. n y. .s"'*LD yJ" -. .. , ---__ ---------, -__- -__-__ __-_ -__- - ___-= to build new information agents that access existing web sources. Ariadne also makes it easy to maintain these agents and incorporate new sources as they become available.