1996

1996
M. Tambe, W. L. Johnson, and W. Shen. 1996. “ Adaptive agent tracking in real-world multiagent domains: A preliminary report.” In AAAI Spring Symposium on Adaptation, Coevolution and Learning in Multiagent Systems.Abstract
In multi-agent environments, the task of agent track-ing (i.e., tracking other agents’ mental states) in-creases in difficulty when a tracker (tracking agent)only has an imperfect model of the trackee (trackedagent). Such model imperfections arise in many real-world situations, where a tracker faces resource con-straints and imperfect information, and the trackeesthemselves modify their behaviors dynamically. Whilesuch model imperfections are unavoidable, a trackermust nonetheless attempt to be adaptive in its agenttracking. In this paper, we analyze some key issuesin adaptive agent tracking, and describe an initial ap-proach based on discrimination-based learning. Themain idea is to identify the deficiency of a model basedon prediction failures, and revise the model by usingfeatures that are critical in discriminating successfuland failed episodes. Our preliminary experiments insimulated air-to-air combat environments have shownsome promising results but many problems remainopen for future research.
1996_8_teamcore_ss96_01_018.pdf
Milind Tambe and P. S. Rosenbloom. 1996. “Event tracking in a dynamic multi-agent environment.” Computational Intelligence 12.Abstract
This paper focuses on event tracking in one complex and dynamic multi-agent environment: the air-combat simulation environment. It analyzes the challenges that an automated pilot agent must face when tracking events in this environment. This analysis reveals three new issues that have not been addressed in previous work in this area: (i) tracking events generated by agents' flexible and reactive behaviors, (ii) tracking events in the context of continuous agent interactions, and (iii) tracking events in real-time. The paper proposes one solution to address these issues. One key idea in this solution is that the (architectural) mechanisms that an agent employs in generating its own flexible and reactive behaviors can be used to track other agents' flexible and reactive behaviors in real-time. A second key idea is the use of a world-centered representation for modeling agent interactions. The solution is demonstrated using an implementation of an automated pilot agent.
1996_4_teamcore_final3.pdf
Milind Tambe. 1996. “ Executing Team Plans in Dynamic Multi-agent environments.” In AAAI Fall Symposium on Plan Execution.Abstract
This paper focuses on flexible teamwork in dynamic and real-world multi-agent domains. Such teamwork is not simply a union of agents’ simultaneous execution of individual plans,even if such execution pre-coordinated. Indeed, uncertain-ties in complex, dynamic domains often obstruct pre-plannedcoordination, with a resultant breakdown in teamwork. Thecentral hypothesis in this paper is that for durable teamwork,agents should be provided explicit team plans, which directlyexpress a team’s joint activities. When agents execute such team plans, they abide by certain "commonsense" conventions of teamwork. Essentially, such conventions providea deeper model of teamwork, facilitating flexible reasoningabout coordination activities. Such a framework also frees the planner or the knowledge engineer from specifying very detailed low-level coordination plans. This framework has been implemented in the context of a real-world synthetic environment for helicopter-combat simulation.
1996_2_teamcore_team_plan2_final.pdf
1996. Intelligent Agents: Vol II, Workshop on Agents, theories, architectures and languages (ATAL-95). Publisher's VersionAbstract
This book is based on the second International Workshop on Agent Theories, Architectures, and Languages, held in conjunction with the International Joint Conference on Artificial Intelligence, IJCAI'95 in Montreal, Canada in August 1995.
The 26 papers are revised final versions of the workshop presentations selected from a total of 54 submissions; also included is a comprehensive introduction, a detailed bibliography listing 355 relevant publications, and a subject index. The book is structured into seven sections, reflecting the most current major directions in agent-related research. Together with its predecessor, Intelligent Agents, published as volume 890 in the LNAI series, this book provides a timely and comprehensive state-of-the-art report.
Milind Tambe. 1996. “ Teamwork in real-world, dynamic environments.” In International conference on multi-agent systems (ICMAS96).Abstract
Flexibleteamworkinreal-worldmulti-agentdomainsismorethana unionofagents’simultaneousexecutionofindividualplans,evenif suchexecutionpre-coordinated.Indeed,uncertaintiesincomplex,dynamicdomainsoftenobstructpre-plannedcoordination,witha resultantbreakdowninteamwork.Thecentralhypothesisinthispaperis thatfordurableteamwork,agentsshouldbeprovidedexplicitteamplansandanunderlyingmodelof teamworkthatexplicitlyoutlinestheircommitmentsandresponsibilitiesasparticipantsin teamactivities.Sucha modelenablesteammemberstoflexiblyreasonaboutcoordinationactivities.Theunderlyingmodelwehaveprovidedisbasedonthejointintentionsframework;althoughwepresentsomekeymodificationstoreflectthepracticalconstraintsin(some)real-worlddomains.Thisframeworkhasbeenimplementedinthecontextofa real-worldsyntheticenvironmentforhelicopter-combatsimulation;someempiricalresultsarepresented.
1996_1_teamcore_tambe_teamwork_1996.pdf
Milind Tambe. 1996. “ Tracking dynamic team activity.” In National Conference on Artificial Intelligence (AAAI96).Abstract
AI researchers are striving to build complex multi-agent worlds with intended applications ranging from the RoboCup robotic soccer tournaments, to interactive virtual theatre, to large-scale real-world battlefield simulations. Agent tracking --- monitoring other agent's actions and inferring their higher-level goals and intentions --- is a central requirement in such worlds. While previous work has mostly focused on tracking individual agents, this paper goes beyond by focusing on agent teams. Team tracking poses the challenge of tracking a team's joint goals and plans. Dynamic, real-time environments add to the challenge, as ambiguities have to be resolved in real-time.
1996_6_teamcore_aaai96team_final.pdf
Milind Tambe. 1996. “ Tracking dynamic team activity: An extended report.” USC ISI Technical report RR, Pp. 96-435.Abstract
AI researchers are striving to build complex multi-agent worlds with intended applications ranging from the RoboCup robotic soccer tournaments, to interactive virtual theatre, to large-scale real-world battlefield simulations. Agent tracking --- monitoring other agent's actions and inferring their higher-level goals and intentions --- is a central requirement in such worlds. While previous work has mostly focused on tracking individual agents, this paper goes beyond by focusing on agent teams. Team tracking poses the challenge of tracking a team's joint goals and plans. Dynamic, real-time environments add to the challenge, as ambiguities have to be resolved in real-time.
1996_7_teamcore_rr_96_435.pdf
Milind Tambe and P. S. Rosenbloom. 1996. “Architectures for agents that track other agents in multi-agent worlds.” In Intelligent Agents, Vol II Springer Verlag Lecture Notes in Artificial Intelligence (LNAI 1037).Abstract
In multi-agent environments, an intelligent agent often needs to interact with other individuals or groups of agents to achieve its goals. Agent tracking is one key capability required for intelligent interaction. It involves monitoring the observable actions of other agents and inferring their unobserved actions, plans, goals and behaviors. This article examines the implications of such an agent tracking capability for agent architectures. It specifically focuses on real-time and dynamic environments, where an intelligent agent is faced with the challenge of tracking the highly flexible mix of goal-driven and reactive behaviors of other agents, in real-time. The key implication is that an agent architecture needs to provide direct support for flexible and efficient reasoning about other agents' models. In this article, such support takes the form of an architectural capability to execute the other agent's models, enabling mental simulation of their behaviors. Other architectural requirements that follow include the capabilities for (pseudo-) simultaneous execution of multiple agent models, dynamic sharing and unsharing of multiple agent models and high bandwidth inter-model communication. We have implemented an agent architecture, an experimental variant of the Soar integrated architecture, that conforms to all of these requirements. Agents based on this architecture have been implemented to execute two different tasks in a real-time, dynamic, multi-agent domain. The article presents experimental results illustrating the agents' dynamic behavior.
1996_3_teamcore_atal2.pdf