scholarly journals Representing and Reasoning with Event Models for Epistemic Planning

2021 ◽  
Author(s):  
David Rajaratnam ◽  
Michael Thielscher
Keyword(s):  
Independent Set ◽  
Description Language ◽  
Event Model ◽  
Multi Agent ◽  
Actual Change ◽  
Epistemic Change ◽  
Event Models ◽  
High Level ◽  
Partially Observable ◽  
Representation Formalism

The standard representation formalism for multi-agent epistemic planning has one central disadvantage: When you use event models in dynamic epistemic logic (DEL) to describe the action of one agent, the model must specify not only the actual change and the change of that agent's knowledge. Also required is the epistemic change of any agents that may be observing the first agent performing the action, plus the epistemic change for any further agents that failed to observe that anything had taken place. To overcome the gap between this complex DEL notion of events and a more commonsense notion of actions, we propose a simple high-level action description language for multi-agent epistemic planning domains with just one type of effect laws: a causes x if y. Effect x can either be a physical effect, or an observation from an independent set that is specific to individual agents. We formally prove that any DEL event model can be described in this way. We show how this language provides a framework for expressing a variety of executability and action models; such as describing actions that are both ontic and epistemic, partially observable, or nondeterministic. We further combine our representation of event models with a description language for finitary initial epistemic theories, and we show how this allows us to reason about the effects of a sequence of actions in a multi-agent epistemic domain by updating a single multi-pointed epistemic model.

scholarly journals Representing and Reasoning About the Rules of General Games With Imperfect Information

2014 ◽  
Vol 49 ◽  
pp. 171-206 ◽  
Author(s):  
S. Schiffel ◽  
M. Thielscher
Keyword(s):  
Imperfect Information ◽  
Complete Information ◽  
Game Playing ◽  
Description Language ◽  
General Game Playing ◽  
Game State ◽  
High Level ◽  
Game Description Language ◽  
General Game ◽  
Representation Formalism

A general game player is a system that can play previously unknown games just by being given their rules. For this purpose, the Game Description Language (GDL) has been developed as a high-level knowledge representation formalism to communicate game rules to players. In this paper, we address a fundamental limitation of state-of-the-art methods and systems for General Game Playing, namely, their being confined to deterministic games with complete information about the game state. We develop a simple yet expressive extension of standard GDL that allows for formalising the rules of arbitrary finite, n-player games with randomness and incomplete state knowledge. In the second part of the paper, we address the intricate reasoning challenge for general game-playing systems that comes with the new description language. We develop a full embedding of extended GDL into the Situation Calculus augmented by Scherl and Levesque's knowledge fluent. We formally prove that this provides a sound and complete reasoning method for players' knowledge about game states as well as about the knowledge of the other players.

scholarly journals IPOMDP-Net: A Deep Neural Network for Partially Observable Multi-Agent Planning Using Interactive POMDPs

2019 ◽  
Vol 33 ◽  
pp. 6062-6069 ◽  
Author(s):  
Yanlin Han ◽  
Piotr Gmytrasiewicz
Keyword(s):  
Neural Network ◽  
Network Architecture ◽  
State Of The Art ◽  
Neural Computing ◽  
Neural Network Architecture ◽  
Markov Decision ◽  
Planning Algorithm ◽  
Multi Agent ◽  
Partially Observable ◽  
Multi Agent Planning

This paper introduces the IPOMDP-net, a neural network architecture for multi-agent planning under partial observability. It embeds an interactive partially observable Markov decision process (I-POMDP) model and a QMDP planning algorithm that solves the model in a neural network architecture. The IPOMDP-net is fully differentiable and allows for end-to-end training. In the learning phase, we train an IPOMDP-net on various fixed and randomly generated environments in a reinforcement learning setting, assuming observable reinforcements and unknown (randomly initialized) model functions. In the planning phase, we test the trained network on new, unseen variants of the environments under the planning setting, using the trained model to plan without reinforcements. Empirical results show that our model-based IPOMDP-net outperforms the other state-of-the-art modelfree network and generalizes better to larger, unseen environments. Our approach provides a general neural computing architecture for multi-agent planning using I-POMDPs. It suggests that, in a multi-agent setting, having a model of other agents benefits our decision-making, resulting in a policy of higher quality and better generalizability.

A Multi-Agent-Based Research on Tourism Supply Chain Risk Management

2014 ◽  
Vol 13 (03) ◽  
pp. 133-153 ◽  
Author(s):  
Luo Biao ◽  
Wan Liang ◽  
Liang Liang
Keyword(s):  
Risk Management ◽  
Supply Chain ◽  
Tourism Industry ◽  
Limiting Factors ◽  
Supply Chain Risk Management ◽  
Agent Based ◽  
Research Perspective ◽  
Multi Agent ◽  
High Level ◽  
Tourism Supply Chain

The high level of complexity of tourism supply chain and the inherent risks that exist in the demand and supply of resources are viewed as major limiting factors in achieving high level performance. Though emerging literature on risk management in tourism industry or its equivalent exists, progress in this area is uneven, as most research focuses on this problem from the traditional single business risk management perspective, without considering the entire range of different suppliers involved in the provision and consumption of tourism products. This study applies risk management theory to a new research perspective, which is tourism supply chain management (SCM). This paper develops a framework for the design of a multi-agent-based decision support system (DSS) based on multi-agent theory and technique, in order to manage disruptions and mitigate risks in tourism supply chain.

A High-Level Policy Description Language for the Network ACL

2004 ◽  
pp. 748-751 ◽  
Author(s):  
Jangha Kim ◽  
Kanghee Lee ◽  
Sangwook Kim ◽  
Jungtaek Seo ◽  
Eunyoung Lee ◽  
...  
Keyword(s):  
Description Language ◽  
Policy Description Language ◽  
High Level

Agent Support for Design of Aircraft Parts

1998 ◽  
Author(s):  
Krishna N. Jha ◽  
Andrea Morris ◽  
Ed Mytych ◽  
Judith Spering
Keyword(s):  
Preliminary Design ◽  
Formal Representation ◽  
Web Based ◽  
Agent Based ◽  
Multi Agent ◽  
High Level ◽  
Legacy Applications ◽  
The Cost ◽  
And Control ◽  
Task Structures

Abstract Designing aircraft parts requires extensive coordination among multiple distributed design groups. Achieving such a coordination is time-consuming and expensive, but the cost of ignoring or minimizing it is much higher in terms of delayed and inferior quality products. We have built a multi-agent-based system to provide the desired coordination among the design groups, the legacy applications, and other resources during the preliminary design (PD) process. A variety of agents are used to model the various design and control functionalities. The agent-representation includes a formal representation of the task-structures. A web-based user-interface provides high-level interface to the users. The agents collaborate to achieve the design goals.

A Formal Description Language for Multi-Agent Architectures

2008 ◽  
pp. 143-163 ◽  
Author(s):  
Stéphane Faulkner ◽  
Manuel Kolp ◽  
Yves Wautelet ◽  
Youssef Achbany
Keyword(s):  
Formal Description ◽  
Description Language ◽  
Agent Architectures ◽  
Multi Agent
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