749 research outputs found
Backdoor Attacks on Multiagent Collaborative Systems
Backdoor attacks on reinforcement learning implant a backdoor in a victim
agent's policy. Once the victim observes the trigger signal, it will switch to
the abnormal mode and fail its task. Most of the attacks assume the adversary
can arbitrarily modify the victim's observations, which may not be practical.
One work proposes to let one adversary agent use its actions to affect its
opponent in two-agent competitive games, so that the opponent quickly fails
after observing certain trigger actions. However, in multiagent collaborative
systems, agents may not always be able to observe others. When and how much the
adversary agent can affect others are uncertain, and we want the adversary
agent to trigger others for as few times as possible. To solve this problem, we
first design a novel training framework to produce auxiliary rewards that
measure the extent to which the other agents'observations being affected. Then
we use the auxiliary rewards to train a trigger policy which enables the
adversary agent to efficiently affect the others' observations. Given these
affected observations, we further train the other agents to perform abnormally.
Extensive experiments demonstrate that the proposed method enables the
adversary agent to lure the others into the abnormal mode with only a few
actions.Comment: 11 page
Decision-making with Imaginary Opponent Models
Opponent modeling has benefited a controlled agent's decision-making by
constructing models of other agents. Existing methods commonly assume access to
opponents' observations and actions, which is infeasible when opponents'
behaviors are unobservable or hard to obtain. We propose a novel multi-agent
distributional actor-critic algorithm to achieve imaginary opponent modeling
with purely local information (i.e., the controlled agent's observations,
actions, and rewards). Specifically, the actor maintains a speculated belief of
the opponents, which we call the \textit{imaginary opponent models}, to predict
opponents' actions using local observations and makes decisions accordingly.
Further, the distributional critic models the return distribution of the
policy. It reflects the quality of the actor and thus can guide the training of
the imaginary opponent model that the actor relies on. Extensive experiments
confirm that our method successfully models opponents' behaviors without their
data and delivers superior performance against baseline methods with a faster
convergence speed.Comment: 13 pages, 27 figure
Data-Driven Design-by-Analogy: State of the Art and Future Directions
Design-by-Analogy (DbA) is a design methodology wherein new solutions,
opportunities or designs are generated in a target domain based on inspiration
drawn from a source domain; it can benefit designers in mitigating design
fixation and improving design ideation outcomes. Recently, the increasingly
available design databases and rapidly advancing data science and artificial
intelligence technologies have presented new opportunities for developing
data-driven methods and tools for DbA support. In this study, we survey
existing data-driven DbA studies and categorize individual studies according to
the data, methods, and applications in four categories, namely, analogy
encoding, retrieval, mapping, and evaluation. Based on both nuanced organic
review and structured analysis, this paper elucidates the state of the art of
data-driven DbA research to date and benchmarks it with the frontier of data
science and AI research to identify promising research opportunities and
directions for the field. Finally, we propose a future conceptual data-driven
DbA system that integrates all propositions.Comment: A Preprint Versio
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