31,240 research outputs found
Algebras for Agent Norm-Regulation
An abstract architecture for idealized multi-agent systems whose behaviour is
regulated by normative systems is developed and discussed. Agent choices are
determined partially by the preference ordering of possible states and
partially by normative considerations: The agent chooses that act which leads
to the best outcome of all permissible actions. If an action is non-permissible
depends on if the result of performing that action leads to a state satisfying
a condition which is forbidden, according to the norms regulating the
multi-agent system. This idea is formalized by defining set-theoretic
predicates characterizing multi-agent systems. The definition of the predicate
uses decision theory, the Kanger-Lindahl theory of normative positions, and an
algebraic representation of normative systems.Comment: 25 page
Homo Socionicus: a Case Study of Simulation Models of Norms
This paper describes a survey of normative agent-based social simulation models. These models are examined from the perspective of the foundations of social theory. Agent-based modelling contributes to the research program of methodological individualism. Norms are a central concept in the role theoretic concept of action in the tradition of Durkheim and Parsons. This paper investigates to what extend normative agent-based models are able to capture the role theoretic concept of norms. Three methodological core problems are identified: the question of norm transmission, normative transformation of agents and what kind of analysis the models contribute. It can be shown that initially the models appeared only to address some of these problems rather than all of them simultaneously. More recent developments, however, show progress in that direction. However, the degree of resolution of intra agent processes remains too low for a comprehensive understanding of normative behaviour regulation.Norms, Normative Agent-Based Social Simulation, Role Theory, Methodological Individualism
Academic Panel: Can Self-Managed Systems be trusted?
Trust can be defined as to have confidence or faith in; a form of reliance or certainty based on past experience; to allow without fear; believe; hope: expect and wish; and extend credit to. The issue of trust in computing has always been a hot topic, especially notable with the proliferation of services over the Internet, which has brought the issue of trust and security right into the ordinary home. Autonomic computing brings its own complexity to this. With systems that self-manage, the internal decision making process is less transparent and the âintelligenceâ possibly evolving and becoming less tractable. Such systems may be used from anything from environment monitoring to looking after Granny in the home and thus the issue of trust is imperative. To this end, we have organised this panel to examine some of the key aspects of trust. The first section discusses the issues of self-management when applied across organizational boundaries. The second section explores predictability in self-managed systems. The third part examines how trust is manifest in electronic service communities. The final discussion demonstrates how trust can be integrated into an autonomic system as the core intelligence with which to base adaptivity choices upon
Designing Normative Theories for Ethical and Legal Reasoning: LogiKEy Framework, Methodology, and Tool Support
A framework and methodology---termed LogiKEy---for the design and engineering
of ethical reasoners, normative theories and deontic logics is presented. The
overall motivation is the development of suitable means for the control and
governance of intelligent autonomous systems. LogiKEy's unifying formal
framework is based on semantical embeddings of deontic logics, logic
combinations and ethico-legal domain theories in expressive classic
higher-order logic (HOL). This meta-logical approach enables the provision of
powerful tool support in LogiKEy: off-the-shelf theorem provers and model
finders for HOL are assisting the LogiKEy designer of ethical intelligent
agents to flexibly experiment with underlying logics and their combinations,
with ethico-legal domain theories, and with concrete examples---all at the same
time. Continuous improvements of these off-the-shelf provers, without further
ado, leverage the reasoning performance in LogiKEy. Case studies, in which the
LogiKEy framework and methodology has been applied and tested, give evidence
that HOL's undecidability often does not hinder efficient experimentation.Comment: 50 pages; 10 figure
Multi-task Deep Reinforcement Learning with PopArt
The reinforcement learning community has made great strides in designing
algorithms capable of exceeding human performance on specific tasks. These
algorithms are mostly trained one task at the time, each new task requiring to
train a brand new agent instance. This means the learning algorithm is general,
but each solution is not; each agent can only solve the one task it was trained
on. In this work, we study the problem of learning to master not one but
multiple sequential-decision tasks at once. A general issue in multi-task
learning is that a balance must be found between the needs of multiple tasks
competing for the limited resources of a single learning system. Many learning
algorithms can get distracted by certain tasks in the set of tasks to solve.
Such tasks appear more salient to the learning process, for instance because of
the density or magnitude of the in-task rewards. This causes the algorithm to
focus on those salient tasks at the expense of generality. We propose to
automatically adapt the contribution of each task to the agent's updates, so
that all tasks have a similar impact on the learning dynamics. This resulted in
state of the art performance on learning to play all games in a set of 57
diverse Atari games. Excitingly, our method learned a single trained policy -
with a single set of weights - that exceeds median human performance. To our
knowledge, this was the first time a single agent surpassed human-level
performance on this multi-task domain. The same approach also demonstrated
state of the art performance on a set of 30 tasks in the 3D reinforcement
learning platform DeepMind Lab
A Cognitive Model for Conversation
International audienceThis paper describes a symbolic model of rational action and decision making to support analysing dialogue. The model approximates principles of behaviour from game theory, and its proof theory makes Gricean principles of cooperativity derivable when the agentsâ preferences align
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