scholarly journals Coalition logic with individual, distributed and common knowledge1

2018 ◽  
Vol 29 (7) ◽  
pp. 1041-1069 ◽  
Author(s):  
Thomas Ågotnes ◽  
Natasha Alechina
Keyword(s):  
Common Knowledge ◽  
Satisfiability Problem ◽  
Multi Agent Systems ◽  
Distributed Knowledge ◽  
Agent Systems ◽  
Coalition Logic ◽  
Multi Agent

Abstract Coalition logic is currently one of the most popular logics for multi-agent systems. While logics combining coalitional and epistemic operators have received considerable attention, completeness results for epistemic extensions of coalition logic have so far been missing. In this paper we provide several such results and proofs. We prove completeness for epistemic coalition logic with common knowledge, with distributed knowledge, and with both common and distributed knowledge, respectively. Furthermore, we completely characterise the complexity of the satisfiability problem for each of the three logics. We also study logics with interaction axioms connecting coalitional ability and knowledge.

scholarly journals DYNAMIC CONSEQUENCE AND PUBLIC ANNOUNCEMENT

2013 ◽  
Vol 6 (4) ◽  
pp. 659-679 ◽  
Author(s):  
ANDRÉS CORDÓN FRANCO ◽  
HANS VAN DITMARSCH ◽  
ANGEL NEPOMUCENO
Keyword(s):  
Common Knowledge ◽  
Consequence Relations ◽  
Multi Agent Systems ◽  
Consequence Relation ◽  
Agent Systems ◽  
Public Announcement ◽  
Public Announcement Logic ◽  
Multi Agent ◽  
Image Position ◽  
Open Question

AbstractIn van Benthem (2008), van Benthem proposes a dynamic consequence relation defined as ${\psi _1}, \ldots ,{\psi _n}{ \models ^d}\phi \,{\rm{iff}}{ \models ^{pa}}[{\psi _1}] \ldots [{\psi _n}]\phi ,$ where the latter denotes consequence in public announcement logic, a dynamic epistemic logic. In this paper we investigate the structural properties of a conditional dynamic consequence relation $\models _{\rm{\Gamma }}^d$ extending van Benthem’s proposal. It takes into account a set of background conditions Γ, inspired by Makinson (2003) wherein Makinson calls this reasoning ‘modulo’ a set Γ. In the presence of common knowledge, conditional dynamic consequence is definable from (unconditional) dynamic consequence. An open question is whether dynamic consequence is compact. We further investigate a dynamic consequence relation for soft instead of hard announcements. Surprisingly, it shares many properties with (hard) dynamic consequence. Dynamic consequence relations provide a novel perspective on reasoning about protocols in multi-agent systems.

ViSe2 - An Agent-Based Expert Consulting System with Efficient Cooperation

1998 ◽  
Vol 2 (3) ◽  
pp. 104-110
Author(s):  
Cheng-Gang Bian ◽  
◽  
Wen Cao ◽  
Gunnar Hartvigsen
Keyword(s):  
Artificial Intelligence ◽  
Software Agents ◽  
Distributed Artificial Intelligence ◽  
Multi Agent Systems ◽  
Current Version ◽  
Distributed Knowledge ◽  
Agent Based ◽  
Agent Systems ◽  
Distributed Problem Solving ◽  
Multi Agent

ViSe2 l is an expert consulting system which employs software agents to manage distributed knowledge sources. These individual software agents solve users’ problems either by themselves or via cooperation. The efficiency of cooperation plays a serious role in Distributed Problem Solving (DPS) and Multi-Agent Systems (MAS). We have focused on the development of a twin-base approach for agents to model the capabilities of each other, and thus achieve efficient cooperation. The current version of the ViSe2 implementation is an experimental model of an agent-based expert system. Compared with other cooperation approaches in Distributed Artificial Intelligence (DAI) area, the results received so far indicate that the ViSe2 agents serve their users in an efficient cooperation manner.

A complete coalition logic of temporal knowledge for multi-agent systems

2014 ◽  
Vol 9 (1) ◽  
pp. 75-86 ◽  
Author(s):  
Qingliang Chen ◽  
Kaile Su ◽  
Yong Hu ◽  
Guiwu Hu
Keyword(s):  
Multi Agent Systems ◽  
Agent Systems ◽  
Coalition Logic ◽  
Multi Agent

APPLYING CONNECTIONIST MODAL LOGICS TO DISTRIBUTED KNOWLEDGE REPRESENTATION PROBLEMS

2004 ◽  
Vol 13 (01) ◽  
pp. 115-139 ◽  
Author(s):  
ARTUR S. d'AVILA GARCEZ ◽  
LUÍS C. LAMB ◽  
KRYSIA BRODA ◽  
DOV M. GABBAY
Keyword(s):  
Neural Networks ◽  
Knowledge Representation ◽  
Modal Logics ◽  
Multi Agent Systems ◽  
Distributed Knowledge ◽  
Learning Capability ◽  
Agent Systems ◽  
Multi Agent ◽  
Full Solution ◽  
Over Time

Neural-Symbolic Systems concern the integration of the symbolic and connectionist paradigms of Artificial Intelligence. Distributed knowledge representation is traditionally seen under a symbolic perspective. In this paper, we show how neural networks can represent distributed symbolic knowledge, acting as multi-agent systems with learning capability (a key feature of neural networks). We apply the framework of Connectionist Modal Logics to well-known testbeds for distributed knowledge representation formalisms, namely the muddy children and the wise men puzzles. Finally, we sketch a full solution to these problems by extending our approach to deal with knowledge evolution over time.

scholarly journals Model Checking Multi-Agent Systems against LDLK Specifications

2017 ◽  
Author(s):  
Jeremy Kong ◽  
Alessio Lomuscio
Keyword(s):  
Model Checking ◽  
Common Knowledge ◽  
Source Model ◽  
Multi Agent System ◽  
Multi Agent Systems ◽  
Agent Systems ◽  
Knowledge States ◽  
Multi Agent ◽  
Epistemic Modalities ◽  
Alternating Automata

We define the logic LDLK, a formalism for specifying multi-agent systems. LDLK extends LDL with epistemic modalities, including common knowledge, for reasoning about the evolution of knowledge states of the agents in the system. We study the complexity of verifying a multi-agent system against LDLK specifications and show this to be in PSPACE. We give an algorithm for the practical verification of multi-agent systems specified in LDLK. We show that the model checking algorithm, based on alternating-automata and nFA, is amenable to symbolic implementation on OBDDs. We introduce MCMAS LDLK , an extension of the open-source model checker MCMAS, implementing the algorithm and discuss the experimental results obtained.

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