844 research outputs found
DM: Decentralized Multi-Agent Reinforcement Learning for Distribution Matching
Current approaches to multi-agent cooperation rely heavily on centralized
mechanisms or explicit communication protocols to ensure convergence. This
paper studies the problem of distributed multi-agent learning without resorting
to centralized components or explicit communication. It examines the use of
distribution matching to facilitate the coordination of independent agents. In
the proposed scheme, each agent independently minimizes the distribution
mismatch to the corresponding component of a target visitation distribution.
The theoretical analysis shows that under certain conditions, each agent
minimizing its individual distribution mismatch allows the convergence to the
joint policy that generated the target distribution. Further, if the target
distribution is from a joint policy that optimizes a cooperative task, the
optimal policy for a combination of this task reward and the distribution
matching reward is the same joint policy. This insight is used to formulate a
practical algorithm (DM), in which each individual agent matches a target
distribution derived from concurrently sampled trajectories from a joint expert
policy. Experimental validation on the StarCraft domain shows that combining
(1) a task reward, and (2) a distribution matching reward for expert
demonstrations for the same task, allows agents to outperform a naive
distributed baseline. Additional experiments probe the conditions under which
expert demonstrations need to be sampled to obtain the learning benefits
Sequential decision making in artificial musical intelligence
Over the past 60 years, artificial intelligence has grown from a largely academic field of research to a ubiquitous array of tools and approaches used in everyday technology. Despite its many recent successes and growing prevalence, certain meaningful facets of computational intelligence have not been as thoroughly explored. Such additional facets cover a wide array of complex mental tasks which humans carry out easily, yet are difficult for computers to mimic. A prime example of a domain in which human intelligence thrives, but machine understanding is still fairly limited, is music. Over the last decade, many researchers have applied computational tools to carry out tasks such as genre identification, music summarization, music database querying, and melodic segmentation. While these are all useful algorithmic solutions, we are still a long way from constructing complete music agents, able to mimic (at least partially) the complexity with which humans approach music. One key aspect which hasn't been sufficiently studied is that of sequential decision making in musical intelligence. This thesis strives to answer the following question: Can a sequential decision making perspective guide us in the creation of better music agents, and social agents in general? And if so, how? More specifically, this thesis focuses on two aspects of musical intelligence: music recommendation and human-agent (and more generally agent-agent) interaction in the context of music. The key contributions of this thesis are the design of better music playlist recommendation algorithms; the design of algorithms for tracking user preferences over time; new approaches for modeling people's behavior in situations that involve music; and the design of agents capable of meaningful interaction with humans and other agents in a setting where music plays a roll (either directly or indirectly). Though motivated primarily by music-related tasks, and focusing largely on people's musical preferences, this thesis also establishes that insights from music-specific case studies can also be applicable in other concrete social domains, such as different types of content recommendation. Showing the generality of insights from musical data in other contexts serves as evidence for the utility of music domains as testbeds for the development of general artificial intelligence techniques. Ultimately, this thesis demonstrates the overall usefulness of taking a sequential decision making approach in settings previously unexplored from this perspectiveComputer Science
Autonomy and Intelligence in the Computing Continuum: Challenges, Enablers, and Future Directions for Orchestration
Future AI applications require performance, reliability and privacy that the
existing, cloud-dependant system architectures cannot provide. In this article,
we study orchestration in the device-edge-cloud continuum, and focus on AI for
edge, that is, the AI methods used in resource orchestration. We claim that to
support the constantly growing requirements of intelligent applications in the
device-edge-cloud computing continuum, resource orchestration needs to embrace
edge AI and emphasize local autonomy and intelligence. To justify the claim, we
provide a general definition for continuum orchestration, and look at how
current and emerging orchestration paradigms are suitable for the computing
continuum. We describe certain major emerging research themes that may affect
future orchestration, and provide an early vision of an orchestration paradigm
that embraces those research themes. Finally, we survey current key edge AI
methods and look at how they may contribute into fulfilling the vision of
future continuum orchestration.Comment: 50 pages, 8 figures (Revised content in all sections, added figures
and new section
Presenting Multiagent Challenges in Team Sports Analytics
This paper draws correlations between several challenges and opportunities
within the area of team sports analytics and key research areas within
multiagent systems (MAS). We specifically consider invasion games, defined as
sports where players invade the opposing team's territory and can interact
anywhere on a playing surface such as ice hockey, soccer, and basketball. We
argue that MAS is well-equipped to study invasion games and will benefit both
MAS and sports analytics fields. Our discussion highlights areas for MAS
implementation and further development along two axes: short-term in-game
strategy (coaching) and long-term team planning (management).Comment: 5 pages, 1 figure, In Proceedings of the 22nd International
Conference on Autonomous Agents and Multiagent Systems (AAMAS 2023
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