2003
Rajiv T. Maheswaran, Milind Tambe, Pradeep Varakantham, and Karen Myers. 2003. “
Adjustable Autonomy challenges in Personal Assistant Agents: A Position Paper .” In Autonomy.
AbstractThe successful integration and acceptance of many multi-agent systems into daily lives
crucially depends on the ability to develop effective policies for adjustable autonomy. Adjustable autonomy encompasses the strategies by which an agent selects the appropriate entity (itself, a human
user, or another agent) to make a decision at key moments when an action is required. We present two
formulations that address this issue: user-based and agent-based autonomy. Furthermore, we discuss
the current and future implications on systems composed of personal assistant agents, where autonomy
issues are of vital interest.
2003_12_teamcore_aabookchapter.pdf Paul Scerri, Pragnesh J. Modi, Milind Tambe, and W. Shen. 2003. “
Are multiagent algorithms relevant for real hardware? A case study of distributed constraint algorithms .” In ACM Symposium on applied computing (SAC'2003).
AbstractResearchers building multi-agent algorithms typically work with
problems abstracted away from real applications. The abstracted
problem instances allow systematic and detailed investigations of
new algorithms. However, a key question is how to apply algorithm, developed on an abstract problem, in a real application. In
this paper, we report on what was required to apply a particular
distributed resource allocation algorithm developed for an abstract
coordination problem in a real hardware application. A probabilistic representation of resources and tasks was used to deal with uncertainty and dynamics and local reasoning was used to deal with
delays in the distributed resource allocation algorithm. The probabilistic representation and local reasoning enabled the use of the
multi-agent algorithm which, in turn, improved the overall performance of the system.
2003_8_teamcore_acm_symp.pdf Pragnesh J. Modi, W. Shen, Milind Tambe, and Makoto Yokoo. 2003. “
An asynchronous complete method for distributed constraint optimization .” In Second International Joint conference on agents and multiagent systems (AAMAS).
AbstractWe present a new polynomial-space algorithm, called Adopt, for distributed
constraint optimization (DCOP). DCOP is able to model a large class of collaboration problems in multi-agent systems where a solution within given
quality parameters must be found. Existing methods for DCOP are not able
to provide theoretical guarantees on global solution quality while operating
both efficiently and asynchronously. Adopt is guaranteed to find an optimal
solution, or a solution within a user-specified distance from the optimal,
while allowing agents to execute asynchronously and in parallel. Adopt obtains these properties via a distributed search algorithm with several novel
characteristics including the ability for each agent to make local decisions
based on currently available information and without necessarily having
global certainty. Theoretical analysis shows that Adopt provides provable
quality guarantees, while experimental results show that Adopt is significantly more efficient than synchronous methods. The speedups are shown
to be partly due to the novel search strategy employed and partly due to the
asynchrony of the algorithm.
2003_11_teamcore_modi_aamas03.pdf D. V. Pynadath and Milind Tambe. 2003. “
Automated teamwork among heterogeneous software agents and humans .” Journal of Autonomous Agents and Multi-Agent Systems (JAAMAS), 7, Pp. 71-100.
AbstractAgent integration architectures enable a heterogeneous, distributed set of agents
to work together to address problems of greater complexity than those addressed by the individual agents themselves. Unfortunately, integrating software agents and humans to perform
real-world tasks in a large-scale system remains difficult, especially due to three main challenges: ensuring robust execution in the face of a dynamic environment, providing abstract task
specifications without all the low-level coordination details, and finding appropriate agents for
inclusion in the overall system. To address these challenges, our Teamcore project provides the
integration architecture with general-purpose teamwork coordination capabilities. We make
each agent team-ready by providing it with a proxy capable of general teamwork reasoning.
Thus, a key novelty and strength of our framework is that powerful teamwork capabilities are
built into its foundations by providing the proxies themselves with a teamwork model.
Given this teamwork model, the Teamcore proxies addresses the first agent integration
challenge, robust execution, by automatically generating the required coordination actions
for the agents they represent. We can also exploit the proxies’ reusable general teamwork
knowledge to address the second agent integration challenge. Through team-oriented programming, a developer specifies a hierarchical organization and its goals and plans, abstracting
away from coordination details. Finally, KARMA, our Knowledgeable Agent Resources Manager Assistant, can aid the developer in conquering the third agent integration challenge by
locating agents that match the specified organization’s requirements. Our integration architecture enables teamwork among agents with no coordination capabilities, and it establishes
and automates consistent teamwork among agents with some coordination capabilities. Thus,
team-oriented programming provides a level of abstraction that can be used on top of previous
approaches to agent-oriented programming. We illustrate how the Teamcore architecture successfully addressed the challenges of agent integration in two application domains: simulated
rehearsal of a military evacuation mission and facilitation of human collaboration.
2003_6_teamcore_jaamas03.pdf Praveen Paruchuri, Milind Tambe, Spiros Kapetanakis, and Sarit Kraus. 2003. “
Between collaboration and competition: An Initial Formalization using Distributed POMDPs .” In GTDT workshop, AAMAS-03. Melbourne, Australia.
AbstractThis paper presents an initial formalization of teamwork in multi-agent domains. Although
analyses of teamwork already exist in the literature of multi-agent systems, almost no work
has dealt with the problem of teams that comprise self-interested agents. The main
contribution of this work is that it concentrates specifically on such teams of self interested
agents. Teams of this kind are common in multi-agent systems as they model the implicit
competition between team members that often arises within a team.
2003_10_teamcore_praveen_aamas03.pdf H. Jung and Milind Tambe. 2003. “
Composing POMDP building blocks to analyze large-scale multiagent systems .” In AAAI Spring Symposium.
Abstract
Given a large group of cooperative agents, selecting the right
coordination or conflict resolution strategy can have a significant impact on their performance (e.g., speed of convergence). While performance models of such coordination or
conflict resolution strategies could aid in selecting the right
strategy for a given domain, such models remain largely uninvestigated in the multiagent literature. This paper takes a step
towards applying the recently emerging distributed POMDP
(partially observable markov decision process) frameworks,
such as the MTDP (markov team decision process) in service
of creating such performance models.
To address issues of scale-up, we use small-scale models,
called building blocks that represent the local interaction
among a small group of agents. We discuss several ways
to combine building blocks for performance prediction of a
larger-scale multiagent system. Our approach is presented in
the context of DCSP (distributed constraint satisfaction problem), where we are able to predict the performance of five different DCSP strategies in different domain settings by modeling and combining building blocks. Our approach points the
way to new tools based on building blocks for performance
analysis in multiagent systems.
2003_13_teamcore_jung_spring_symp.pdf Hyuckchul Jung. 2003. “
Conflict Resolution Strategies and their Performance models for large scale Multi Agent Systems ”.
AbstractDistributed, collaborative agents are promising to play an important role in large-scale
multiagent applications, such as distributed sensors and distributed spacecraft. Since
no single agent can have complete global knowledge in such large scale applications,
conflicts are inevitable even among collaborative agents over shared resources, plans, or
tasks. Fast conflict resolution techniques are required in many multiagent systems under
soft or hard time constraints. In resolving conflicts, we focus on the approaches based
on DCSP (distributed constraint satisfaction problems), a major paradigm in multiagent
conflict resolution. We aim to speed up conflict resolution convergence via developing
efficient DCSP strategies.
We focus on multiagent systems characterized by agents that are collaborative,
homogeneous, arranged in regular networks, and relying on local communication (found
in many multiagent applications). This thesis provides the followings major contributions. First, we develop novel DCSP strategies that significantly speed up conflict resolution convergence. The novel strategies are based on the extra communication of
local information between neighboring agents. We formalize a set of DCSP strategies
which exploit the extra communication: in selecting a new choice of actions, plans,
or resources to resolve conflicts, each agent takes into account how much flexibility is
given to neighboring agents. Second, we provide a new run-time model for performance
measurement of DCSP strategies since a popular existing DCSP performance metric does not consider the extra communication overhead. The run-time model enables us to
evaluate the strategy performance in various computing and networking environments.
Third, the analysis of message processing and communication overhead of the novel
strategies shows that such overhead caused by the novel strategy is not overwhelming.
Thus, despite extra communication, the novel strategies indeed show big speedups in
a significant range of problems (particularly for harder problems). Fourth, we provide
categorization of problem settings with big speedups by the novel strategies Finally,
to select the right strategy in a given domain, we develop performance modeling techniques based on distributed POMDP (Partially Observable Markov Decision Process)
based model where scalability issue is addressed with a new decomposition technique.
2003_15_teamcore_hyuckchul_thesis_final.pdf Pragnesh 'Jay' Modi. 2003. “
Distributed Constraint Optimization of Multi-Agent Systems ”.
AbstractTo coordinate effectively, multiple agents must reason and communicate about the
interactions between their individual local decisions. Distributed planning, distributed
scheduling, distributed resource allocation and distributed task allocation are some examples of multiagent problems where such reasoning is required. In order to represent
these types of automated reasoning problems, researchers in Multiagent Systems have
proposed distributed constraints as a key paradigm. Previous research in Artificial Intelligence and Constraint Programming has shown that constraints are a convenient yet
powerful way to represent automated reasoning problems.
This dissertation advances the state-of-the-art in Multiagent Systems and Constraint
Programming through three key innovations. First, this dissertation introduces a novel
algorithm for Distributed Constraint Optimization Problems (DCOP). DCOP significantly generalizes existing satisfaction-based constraint representations to allow optimization. We present Adopt, the first algorithm for DCOP that allows asynchronous
concurrent execution and is guaranteed to terminate with the globally optimal solution.
The key idea is to perform systematic distributed optimization based on conservative solution quality estimates rather than exact knowledge of global solution quality. This
method is empirically shown to yield orders of magnitude speedups over existing synchronous methods and is shown to be robust to lost messages.
Second, this dissertation introduces bounded-error approximation as a flexible method
whereby agents can find global solutions that may not be optimal but are guaranteed
to be within a given distance from optimal. This method is useful for time-limited domains because it decreases solution time and communication overhead. Bounded-error
approximation is a significant departure from existing incomplete local methods, which
rely exclusively on local information to obtain a decrease in solution time but at the cost
of abandoning all theoretical guarantees on solution quality.
Third, this dissertation presents generalized mapping strategies that allow a significant class of distributed resource allocation problem to be automatically represented via
distributed constraints. These mapping strategies are signficant because they illustrate
the utility of the distributed constraint representation. These mapping strategies are useful because they provide multiagent researchers with a general, reusable methodology
for understanding, representing and solving their own distributed resource allocation
problems. Our theoretical results show the correctness of the mappings.
2003_16_teamcore_modi_thesis.pdf V. Lesser, C. Ortiz, and Milind Tambe. 2003.
Distributed sensor nets: A multiagent perspective . Kluwer academic.
AbstractDistributed Sensor Networks is the first book of its kind to examine solutions to this problem using ideas taken from the field of multiagent systems. The field of multiagent systems has itself seen an exponential growth in the past decade, and has developed a variety of techniques for distributed resource allocation.
Distributed Sensor Networks contains contributions from leading, international researchers describing a variety of approaches to this problem based on examples of implemented systems taken from a common distributed sensor network application; each approach is motivated, demonstrated and tested by way of a common challenge problem. The book focuses on both practical systems and their theoretical analysis, and is divided into three parts: the first part describes the common sensor network challenge problem; the second part explains the different technical approaches to the common challenge problem; and the third part provides results on the formal analysis of a number of approaches taken to address the challenge problem.
2003_18_teamcore_tambe_book.jpg Paul Scerri, Johnson L., D. V. Pynadath, P. S. Rosenbloom, M. Si, Nathan Schurr, and Milind Tambe. 2003. “
Getting robots, agents and people to cooperate: An initial study .” In AAAI Spring Symposium.
AbstractCombining the unique capabilities of robots, agents and people (RAPs) promises to improve the safety, efficiency, reliability and cost at which some goals can be achieved, while allowing the achievement of other goals not previously achievable. Despite their heterogeneity, and indeed, because of their
heterogeneity, our key hypothesis is that in order for RAPs to
work together effectively, they must work as a team: they
should be aware of the overall goals of the team, and coordinate their activities with their fellow team members in order to further the team’s goals. This poses a challenge, since
different RAP entities may have differing social abilities and
hence differing abilities to coordinate with their teammates.
To construct such RAP teams, we make the RAPs “team
ready” by providing each of them with teamwork-enabled
proxies. While proxy-based architectures have been explored
earlier for flexible multiagent teamwork, their application to
RAP teams exposes two weaknesses. To address the problems with existing role allocation algorithms for RAP teams
operating in dynamic environments, we provide a new approach for highly flexible role allocation and reallocation.
Second, we enrich the communication between RAPs and
between a RAP and its proxy, to improve teamwork flexibility while limiting the number of messages exchanged. This
paper discusses the proxy based architecture and our initial
attempts at developing algorithms that address the problems
that emerge when the RAP teams are used in complex domains.
2003_9_teamcore_rap.pdf Ranjit Nair, Milind Tambe, and S. Marsella. 2003. “
Integrating BDI approaches with POMDPs: The case of team-oriented programs .” In AAAI Spring Symposium.
Abstract
Integrating approaches based on belief-desire-intentions
(BDI) logics with the more recent developments of distributed POMDPs is today a fundamental challenge in the
multiagent systems arena. One common suggestion for such
an integration is to use stochastic models (POMDPs) for generating agent behaviors, while using the BDI components for
monitoring and creating explanations. We propose a completely inverse approach, where the BDI components are used
to generate agent behaviors, and distributed POMDPs are
used in an analysis mode. In particular, we focus on teamoriented programs for tasking multiagent teams, where the
team-oriented programs specify hierarchies of team plans that
the team and its subteams must adopt as their joint intentions.
However, given a limited number of agents, finding a good
way to allocate them to different teams and subteams to execute such a team-oriented program is a difficult challenge
We use distributed POMDPs to analyze different allocations
of agents within a team-oriented program, and to suggest improvements to the program. The key innovation is to use
the distributed POMDP analysis not as a black box, but as
a glass box, offering insights into why particular allocations
lead to good or bad outcomes. These insights help to prune
the search space of different allocations, offering significant
speedups in the search. We present preliminary experimental
results to illustrate our methodology.
2003_5_teamcore_nair_spring_symp.pdf Ranjit Nair, Milind Tambe, and Stacy Marsella. 2003. “
Integrating Belief-Desire-Intention Approaches with POMDPs: The Case of Team-Oriented Programs .” In AAAI Spring Symposium on Logical Formalisms for Commonsense Reasoning.
Abstract
Integrating approaches based on belief-desire-intentions
(BDI) logics with the more recent developments of distributed POMDPs is today a fundamental challenge in the
multiagent systems arena. One common suggestion for such
an integration is to use stochastic models (POMDPs) for generating agent behaviors, while using the BDI components for
monitoring and creating explanations. We propose a completely inverse approach, where the BDI components are used
to generate agent behaviors, and distributed POMDPs are
used in an analysis mode. In particular, we focus on teamoriented programs for tasking multiagent teams, where the
team-oriented programs specify hierarchies of team plans that
the team and its subteams must adopt as their joint intentions.
However, given a limited number of agents, finding a good
way to allocate them to different teams and subteams to execute such a team-oriented program is a difficult challenge
We use distributed POMDPs to analyze different allocations
of agents within a team-oriented program, and to suggest improvements to the program. The key innovation is to use
the distributed POMDP analysis not as a black box, but as
a glass box, offering insights into why particular allocations
lead to good or bad outcomes. These insights help to prune
the search space of different allocations, offering significant
speedups in the search. We present preliminary experimental
results to illustrate our methodology.
2003_19_teamcore_nair_tambe_marsella.pdf H. Jung and Milind Tambe. 2003. “
Performance models for large-scale multiagent systems: Using Distributed POMDP building blocks .” In Second International Joint conference on agents and multiagent systems (AAMAS).
Abstract
Given a large group of cooperative agents, selecting the right coordination or conflict resolution strategy can have a significant impact
on their performance (e.g., speed of convergence). While performance models of such coordination or conflict resolution strategies
could aid in selecting the right strategy for a given domain, such
models remain largely uninvestigated in the multiagent literature.
This paper takes a step towards applying the recently emerging distributed POMDP (partially observable Markov decision process)
frameworks, such as MTDP (Markov team decision process), in
service of creating such performance models. To address issues
of scale-up, we use small-scale models, called building blocks that
represent the local interaction among a small group of agents. We
discuss several ways to combine building blocks for performance
prediction of a larger-scale multiagent system.
We present our approach in the context of DCSPs (distributed
constraint satisfaction problems), where we first show that there is
a large bank of conflict resolution strategies and no strategy dominates all others across different domains. By modeling and combining building blocks, we are able to predict the performance of
five different DCSP strategies for four different domain settings,
for a large-scale multiagent system. Our approach thus points the
way to new tools for strategy analysis and performance modeling
in multiagent systems in general.
2003_14_teamcore_jungh_aamas03.pdf Paul Scerri, Johnson L., D. V. Pynadath, P. S. Rosenbloom, M. Si, Nathan Schurr, and Milind Tambe. 2003. “
A prototype infrastructure for distributed robot, agent, person teams .” In Second International Joint conference on agents and multiagent systems (AAMAS).
AbstractEffective coordination of robots, agents and people promises to improve the safety, robustness and quality with which shared goals are
achieved by harnessing the highly heterogeneous entities’ diverse
capabilities. Proxy-based integration architectures are emerging as
a standard method for coordinating teams of heterogeneous entities.
Such architectures are designed to meet imposing challenges such
as ensuring that the diverse capabilities of the group members are
effectively utilized, avoiding miscoordination in a noisy, uncertain
environment and reacting flexibly to changes in the environment.
However, we contend that previous architectures have gone too far
in taking coordination responsibility away from entities and giving
it to proxies. Our goal is to create a proxy-based integration infrastructure where there is a beneficial symbiotic relationship between
the proxies and the team members. By leveraging the coordination
abilities of both proxies and socially capable team members the
quality of the coordination can be improved. We present two key
new ideas to achieve this goal. First, coordination tasks are represented as explicit roles, hence the responsibilities not the actions
are specified, thus allowing the team to leverage the coordination
skills of the most capable team members. Second, building on the
first idea, we have developed a novel role allocation and reallocation algorithm. These ideas have been realized in a prototype software proxy architecture and used to create heterogeneous teams for
an urban disaster recovery domain. Using the rescue domain as a
testbed, we have experimented with the role allocation algorithm
and observed results to support the hypothesis that leveraging the
coordination capabilities of people can help the performance of the
team.
2003_7_teamcore_rap_aamas03.pdf Ranjit Nair, Milind Tambe, and S. Marsella. 2003. “
Role allocation and reallocation in multiagent teams: Towards a practical analysis .” In Second International Joint conference on agents and multiagent systems (AAMAS).
AbstractDespite the success of the BDI approach to agent teamwork, initial
role allocation (i.e. deciding which agents to allocate to key roles
in the team) and role reallocation upon failure remain open challenges. What remain missing are analysis techniques to aid human
developers in quantitatively comparing different initial role allocations and competing role reallocation algorithms. To remedy this
problem, this paper makes three key contributions. First, the paper
introduces RMTDP (Role-based Multiagent Team Decision Problem), an extension to MTDP [9], for quantitative evaluations of role
allocation and reallocation approaches. Second, the paper illustrates an RMTDP-based methodology for not only comparing two
competing algorithms for role reallocation, but also for identifying the types of domains where each algorithm is suboptimal, how
much each algorithm can be improved and at what computational
cost (complexity). Such algorithmic improvements are identified
via a new automated procedure that generates a family of locally
optimal policies for comparative evaluations. Third, since there
are combinatorially many initial role allocations, evaluating each
in RMTDP to identify the best is extremely difficult. Therefore, we
introduce methods to exploit task decompositions among subteams
to significantly prune the search space of initial role allocations.
We present experimental results from two distinct domains.
2003_3_teamcore_nair_aamas03.pdf Ranjit Nair, Milind Tambe, and S. Marsella. 2003. “
The role of emotions in multiagent teamwork .” In Who needs emotions: The brain meets the machine,
edited by J. Fellous and M. Arbib. MIT Press.
AbstractEmotions play a significant role in human teamwork. However, despite the significant progress in multiagent teamwork, as well as progress in computational models of emotions, there have been very few investigations of the role of emotions in multiagent teamwork. This chapter attempts a first step towards addressing this shortcoming. It provides a short survey of the state of the art in multiagent teamwork and in computational models of emotions. It considers three cases of teamwork, in particular, teams of simulated humans, agent-human teams and pure agent teams, and examine the effects of introducing emotions in each. Finally, it also provides preliminary experimental results illustrating the impact of emotions on multiagent teamwork.
2003_4_teamcore_nair_emotions.pdf Ranjit Nair, Milind Tambe, Makoto Yokoo, D. V. Pynadath, and S. Marsella. 2003. “
Taming Decentralized POMDPs: Towards efficient policy computation for multiagent settings .” In International Joint conference on Artificial Intelligence (IJCAI).
Abstract
The problem of deriving joint policies for a group
of agents that maximize some joint reward function can be modeled as a decentralized partially
observable Markov decision process (POMDP).
Yet, despite the growing importance and applications of decentralized POMDP models in the multiagents arena, few algorithms have been developed for efficiently deriving joint policies for these
models. This paper presents a new class of locally optimal algorithms called “Joint Equilibriumbased search for policies (JESP)”. We first describe
an exhaustive version of JESP and subsequently
a novel dynamic programming approach to JESP.
Our complexity analysis reveals the potential for
exponential speedups due to the dynamic programming approach. These theoretical results are verified via empirical comparisons of the two JESP
versions with each other and with a globally optimal brute-force search algorithm. Finally, we prove
piece-wise linear and convexity (PWLC) properties, thus taking steps towards developing algorithms for continuous belief states
2003_2_teamcore_nair_ijcai03.pdf Paul Scerri, David V. Pynadath, Nathan Schurr, Alessandro Farinelli, Sudeep Gandhe, and Milind Tambe. 2003. “
Team Oriented Programming and Proxy Agents: The Next Generation .” In Lecture notes in computer science, Proceedings of the workshop on Programming multiagent systems, Springer.
AbstractCoordination between large teams of highly heterogeneous
entities will change the way complex goals are pursued in real world environments. One approach to achieving the required coordination in such
teams is to give each team member a proxy that assumes routine coordination activities on behalf of its team member. Despite that approach’s
success, as we attempt to apply this first generation of proxy architecture to larger teams in more challenging environments, some limitations
become clear. In this paper, we present initial efforts on the next generation of proxy architecture and Team Oriented Programming (TOP),
called Machinetta. Machinetta aims to overcome the limitations of the
previous generation of proxies and allow effective coordination between
very large teams of highly heterogeneous agents. We describe the principles underlying the design of the Machinetta proxies and present initial
results from two domains.
2003_17_teamcore_10_1_1_69.9607.pdf Pragnesh J. Modi, Syed M. Ali, Rishi Goel, and Milind Tambe. 2003. “
Distributed Constraint Reasoning under Unreliable Communication.” In International workshop on distributed constraint reasoning DCR-03.
AbstractThe Distributed Constraint Optimization Problem (DCOP) is able to model many problems in multiagent systems but existing research has not considered the issue of unreliable communication which often arises in real world applications. Limited bandwidth, interference, loss of line-of-sight are some reasons why communication fails in the real world. In this paper we show that an existing asynchronous algorithm for DCOP can be made to operate effectively in the face of message loss through the introduction of a very simple timeout mechanism for selective communication. Despite its simplicity, this mechanism is shown to dramatically reduce communication overhead while preventing deadlocks that can occur when messages are lost. Results show that the optimal solution can be guaranteed even in the presence of message loss and that algorithm performance measured in terms of time to solution degrades gracefully as message loss probability increases.
2003_1_teamcore_modi_dcr03.pdf