28,102 research outputs found
Expectation-Aware Planning: A Unifying Framework for Synthesizing and Executing Self-Explaining Plans for Human-Aware Planning
In this work, we present a new planning formalism called Expectation-Aware
planning for decision making with humans in the loop where the human's
expectations about an agent may differ from the agent's own model. We show how
this formulation allows agents to not only leverage existing strategies for
handling model differences but can also exhibit novel behaviors that are
generated through the combination of these different strategies. Our
formulation also reveals a deep connection to existing approaches in epistemic
planning. Specifically, we show how we can leverage classical planning
compilations for epistemic planning to solve Expectation-Aware planning
problems. To the best of our knowledge, the proposed formulation is the first
complete solution to decision-making in the presence of diverging user
expectations that is amenable to a classical planning compilation while
successfully combining previous works on explanation and explicability. We
empirically show how our approach provides a computational advantage over
existing approximate approaches that unnecessarily try to search in the space
of models while also failing to facilitate the full gamut of behaviors enabled
by our framework
Explainable Planning
As AI is increasingly being adopted into application solutions, the challenge
of supporting interaction with humans is becoming more apparent. Partly this is
to support integrated working styles, in which humans and intelligent systems
cooperate in problem-solving, but also it is a necessary step in the process of
building trust as humans migrate greater responsibility to such systems. The
challenge is to find effective ways to communicate the foundations of AI-driven
behaviour, when the algorithms that drive it are far from transparent to
humans. In this paper we consider the opportunities that arise in AI planning,
exploiting the model-based representations that form a familiar and common
basis for communication with users, while acknowledging the gap between
planning algorithms and human problem-solving.Comment: Presented at the IJCAI-17 workshop on Explainable AI
(http://home.earthlink.net/~dwaha/research/meetings/ijcai17-xai/). Melbourne,
August 201
Local and Global Explanations of Agent Behavior: Integrating Strategy Summaries with Saliency Maps
With advances in reinforcement learning (RL), agents are now being developed
in high-stakes application domains such as healthcare and transportation.
Explaining the behavior of these agents is challenging, as the environments in
which they act have large state spaces, and their decision-making can be
affected by delayed rewards, making it difficult to analyze their behavior. To
address this problem, several approaches have been developed. Some approaches
attempt to convey the behavior of the agent, describing the
actions it takes in different states. Other approaches devised
explanations which provide information regarding the agent's decision-making in
a particular state. In this paper, we combine global and local explanation
methods, and evaluate their joint and separate contributions, providing (to the
best of our knowledge) the first user study of combined local and global
explanations for RL agents. Specifically, we augment strategy summaries that
extract important trajectories of states from simulations of the agent with
saliency maps which show what information the agent attends to. Our results
show that the choice of what states to include in the summary (global
information) strongly affects people's understanding of agents: participants
shown summaries that included important states significantly outperformed
participants who were presented with agent behavior in a randomly set of chosen
world-states. We find mixed results with respect to augmenting demonstrations
with saliency maps (local information), as the addition of saliency maps did
not significantly improve performance in most cases. However, we do find some
evidence that saliency maps can help users better understand what information
the agent relies on in its decision making, suggesting avenues for future work
that can further improve explanations of RL agents
CBR and MBR techniques: review for an application in the emergencies domain
The purpose of this document is to provide an in-depth analysis of current reasoning engine practice and the integration strategies of Case Based Reasoning and Model Based Reasoning that will be used in the design and development of the RIMSAT system.
RIMSAT (Remote Intelligent Management Support and Training) is a European Commission funded project designed to:
a.. Provide an innovative, 'intelligent', knowledge based solution aimed at improving the quality of critical decisions
b.. Enhance the competencies and responsiveness of individuals and organisations involved in highly complex, safety critical incidents - irrespective of their location.
In other words, RIMSAT aims to design and implement a decision support system that using Case Base Reasoning as well as Model Base Reasoning technology is applied in the management of emergency situations.
This document is part of a deliverable for RIMSAT project, and although it has been done in close contact with the requirements of the project, it provides an overview wide enough for providing a state of the art in integration strategies between CBR and MBR technologies.Postprint (published version
Challenges for a CBR framework for argumentation in open MAS
[EN] Nowadays, Multi-Agent Systems (MAS) are broadening their applications to open environments,
where heterogeneous agents could enter into the system, form agents’ organizations and interact.
The high dynamism of open MAS gives rise to potential conflicts between agents and thus, to a
need for a mechanism to reach agreements. Argumentation is a natural way of harmonizing
conflicts of opinion that has been applied to many disciplines, such as Case-Based Reasoning
(CBR) and MAS. Some approaches that apply CBR to manage argumentation in MAS have been
proposed in the literature. These improve agents’ argumentation skills by allowing them to reason
and learn from experiences. In this paper, we have reviewed these approaches and identified the
current contributions of the CBR methodology in this area. As a result of this work, we have
proposed several open issues that must be taken into consideration to develop a CBR framework
that provides the agents of an open MAS with arguing and learning capabilities.This work was partially supported by CONSOLIDER-INGENIO 2010 under grant CSD2007-00022 and by the Spanish government and FEDER funds under TIN2006-14630-C0301 project.Heras Barberá, SM.; Botti Navarro, VJ.; Julian Inglada, VJ. (2009). Challenges for a CBR framework for argumentation in open MAS. Knowledge Engineering Review. 24(4):327-352. https://doi.org/10.1017/S0269888909990178S327352244Willmott S. , Vreeswijk G. , Chesñevar C. , South M. , McGinnis J. , Modgil S. , Rahwan I. , Reed C. , Simari G. 2006. Towards an argument interchange format for multi-agent systems. In Proceedings of the AAMAS International Workshop on Argumentation in Multi-Agent Systems, ArgMAS-06, 17–34.Sycara, K. P. (1990). Persuasive argumentation in negotiation. Theory and Decision, 28(3), 203-242. doi:10.1007/bf00162699Ontañón S. , Plaza E. 2006. Arguments and counterexamples in case-based joint deliberation. 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