8,465 research outputs found
Motor Resonance as Indicator for Quality of Interaction - Does it Scale to Natural Movements?
This paper is the output of work that is financed by a grant by the Air Force Office for Scientific Research (AFOSR). © 2018 International Foundation for Autonomous Agents and Multiagent Systems (www.ifaamas.org). All rights reserved.Detecting in an automatic manner whether a particular interaction between man and machine “works”, is an unsolved problem in human-machine interaction. No computational technique exists by which the artificial agent could perceive whether the interaction works from the viewpoint of the human or whether interactional breakdown is likely to occur. In human-robot interaction motor resonance has been proposed as a potential candidate for assessing what might be termed “quality of interaction”. Other authors have asserted that “the measure of resonance indicates the extent to which an artificial agent is considered as a social inter-actor” and call it “a plausible foundation for higher-order social cognition”. Motor interference is often used as a metric for resonance. While the above suggests that motor resonance might be suitable as general measure for the potential of an artificial agent to be conceived of as a social entity, the question remains whether it can be used as a measure for the quality of an ongoing interaction
Ordered Preference Elicitation Strategies for Supporting Multi-Objective Decision Making
In multi-objective decision planning and learning, much attention is paid to
producing optimal solution sets that contain an optimal policy for every
possible user preference profile. We argue that the step that follows, i.e,
determining which policy to execute by maximising the user's intrinsic utility
function over this (possibly infinite) set, is under-studied. This paper aims
to fill this gap. We build on previous work on Gaussian processes and pairwise
comparisons for preference modelling, extend it to the multi-objective decision
support scenario, and propose new ordered preference elicitation strategies
based on ranking and clustering. Our main contribution is an in-depth
evaluation of these strategies using computer and human-based experiments. We
show that our proposed elicitation strategies outperform the currently used
pairwise methods, and found that users prefer ranking most. Our experiments
further show that utilising monotonicity information in GPs by using a linear
prior mean at the start and virtual comparisons to the nadir and ideal points,
increases performance. We demonstrate our decision support framework in a
real-world study on traffic regulation, conducted with the city of Amsterdam.Comment: AAMAS 2018, Source code at
https://github.com/lmzintgraf/gp_pref_elici
Detecting Possible Manipulators in Elections
Manipulation is a problem of fundamental importance in the context of voting
in which the voters exercise their votes strategically instead of voting
honestly to prevent selection of an alternative that is less preferred. The
Gibbard-Satterthwaite theorem shows that there is no strategy-proof voting rule
that simultaneously satisfies certain combinations of desirable properties.
Researchers have attempted to get around the impossibility results in several
ways such as domain restriction and computational hardness of manipulation.
However these approaches have been shown to have limitations. Since prevention
of manipulation seems to be elusive, an interesting research direction
therefore is detection of manipulation. Motivated by this, we initiate the
study of detection of possible manipulators in an election.
We formulate two pertinent computational problems - Coalitional Possible
Manipulators (CPM) and Coalitional Possible Manipulators given Winner (CPMW),
where a suspect group of voters is provided as input to compute whether they
can be a potential coalition of possible manipulators. In the absence of any
suspect group, we formulate two more computational problems namely Coalitional
Possible Manipulators Search (CPMS), and Coalitional Possible Manipulators
Search given Winner (CPMSW). We provide polynomial time algorithms for these
problems, for several popular voting rules. For a few other voting rules, we
show that these problems are in NP-complete. We observe that detecting
manipulation maybe easy even when manipulation is hard, as seen for example, in
the case of the Borda voting rule.Comment: Accepted in AAMAS 201
Refining and Delegating Strategic Ability in ATL
We propose extending Alternating-time Temporal Logic (ATL) by an operator <i
refines-to G> F to express that agent i can distribute its powers to a set of
sub-agents G in a way which satisfies ATL condition f on the strategic ability
of the coalitions they may form, possibly together with others agents. We prove
the decidability of model-checking of formulas whose subformulas with this
operator as the main connective have the form ...<i_m
refines-to G_m> f, with no further occurrences of this operator in f.Comment: In Proceedings SR 2014, arXiv:1404.041
A Study of AI Population Dynamics with Million-agent Reinforcement Learning
We conduct an empirical study on discovering the ordered collective dynamics
obtained by a population of intelligence agents, driven by million-agent
reinforcement learning. Our intention is to put intelligent agents into a
simulated natural context and verify if the principles developed in the real
world could also be used in understanding an artificially-created intelligent
population. To achieve this, we simulate a large-scale predator-prey world,
where the laws of the world are designed by only the findings or logical
equivalence that have been discovered in nature. We endow the agents with the
intelligence based on deep reinforcement learning (DRL). In order to scale the
population size up to millions agents, a large-scale DRL training platform with
redesigned experience buffer is proposed. Our results show that the population
dynamics of AI agents, driven only by each agent's individual self-interest,
reveals an ordered pattern that is similar to the Lotka-Volterra model studied
in population biology. We further discover the emergent behaviors of collective
adaptations in studying how the agents' grouping behaviors will change with the
environmental resources. Both of the two findings could be explained by the
self-organization theory in nature.Comment: Full version of the paper presented at AAMAS 2018 (International
Conference on Autonomous Agents and Multiagent Systems
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