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Ad-hoc teamwork with behavior-switching agents
As autonomous AI agents proliferate in the real world, they will increasingly need to cooperate with each other to achieve complex goals without always being able to coordinate in advance. This kind of cooperation, in which agents have to learn to cooperate on the fly, is called ad hoc teamwork. Many previous works investigating this setting assumed that teammates behave according to one of many predefined types that is fixed throughout the task. This assumption of stationarity in behaviors, is a strong assumption which cannot be guaranteed in many real-world settings. In this work, we relax this assumption and investigate settings in which teammates can change their types during the course of the task. This adds complexity to the planning problem as now an agent needs to recognize that a change has occurred in addition to figuring out what is the new type of the teammate it is interacting with. In this paper, we present a novel Convolutional-Neural-Network-based Change Point Detection (CPD) algorithm for ad hoc teamwork. When evaluating our algorithm on the modified predator prey domain, we find that it outperforms existing Bayesian CPD algorithms.Electrical and Computer Engineerin
Individualized Mutual Adaptation in Human-Agent Teams
The ability to collaborate with previously unseen human teammates is crucial for artificial agents to be effective in human-agent teams (HATs). Due to individual differences and complex team dynamics, it is hard to develop a single agent policy to match all potential teammates. In this paper, we study both human-human and humanagent teams in a dyadic cooperative task, Team Space Fortress (TSF). Results show that the team performance is influenced by both players’ individual skill level and their ability to collaborate with different teammates by adopting complementary policies. Based on human-human team results, we propose an adaptive agent that identifies different human policies and assigns a complementary partner policy to optimize team performance. The adaptation method relies on a novel similarity metric to infer human policy and then selects the most complementary policy from a pre-trained library of exemplar policies. We conducted human-agent experiments to evaluate the adaptive agent and examine mutual adaptation in humanagent teams. Results show that both human adaptation and agent adaptation contribute to team performanc
Melting Pot 2.0
Multi-agent artificial intelligence research promises a path to develop
intelligent technologies that are more human-like and more human-compatible
than those produced by "solipsistic" approaches, which do not consider
interactions between agents. Melting Pot is a research tool developed to
facilitate work on multi-agent artificial intelligence, and provides an
evaluation protocol that measures generalization to novel social partners in a
set of canonical test scenarios. Each scenario pairs a physical environment (a
"substrate") with a reference set of co-players (a "background population"), to
create a social situation with substantial interdependence between the
individuals involved. For instance, some scenarios were inspired by
institutional-economics-based accounts of natural resource management and
public-good-provision dilemmas. Others were inspired by considerations from
evolutionary biology, game theory, and artificial life. Melting Pot aims to
cover a maximally diverse set of interdependencies and incentives. It includes
the commonly-studied extreme cases of perfectly-competitive (zero-sum)
motivations and perfectly-cooperative (shared-reward) motivations, but does not
stop with them. As in real-life, a clear majority of scenarios in Melting Pot
have mixed incentives. They are neither purely competitive nor purely
cooperative and thus demand successful agents be able to navigate the resulting
ambiguity. Here we describe Melting Pot 2.0, which revises and expands on
Melting Pot. We also introduce support for scenarios with asymmetric roles, and
explain how to integrate them into the evaluation protocol. This report also
contains: (1) details of all substrates and scenarios; (2) a complete
description of all baseline algorithms and results. Our intention is for it to
serve as a reference for researchers using Melting Pot 2.0.Comment: 59 pages, 54 figures. arXiv admin note: text overlap with
arXiv:2107.0685
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