scholarly journals Disjunctive Normal Form for Multi-Agent Modal Logics Based on Logical Separability

2019 ◽  
Vol 33 ◽  
pp. 2817-2826
Author(s):  
Liangda Fang ◽  
Kewen Wang ◽  
Zhe Wang ◽  
Ximing Wen
Keyword(s):  
Normal Form ◽  
Propositional Logic ◽  
Disjunctive Normal Form ◽  
Modal Logics ◽  
Multi Agent Systems ◽  
Modal Formula ◽  
Agent Systems ◽  
Multi Agent ◽  
Automatic Planning ◽  
Reasoning Tasks

Modal logics are primary formalisms for multi-agent systems but major reasoning tasks in such logics are intractable, which impedes applications of multi-agent modal logics such as automatic planning. One technique of tackling the intractability is to identify a fragment called a normal form of multiagent logics such that it is expressive but tractable for reasoning tasks such as entailment checking, bounded conjunction transformation and forgetting. For instance, DNF of propositional logic is tractable for these reasoning tasks. In this paper, we first introduce a notion of logical separability and then define a novel disjunctive normal form SDNF for the multiagent logic Kn, which overcomes some shortcomings of existing approaches. In particular, we show that every modal formula in Kn can be equivalently casted as a formula in SDNF, major reasoning tasks tractable in propositional DNF are also tractable in SDNF, and moreover, formulas in SDNF enjoy the property of logical separability. To demonstrate the usefulness of our approach, we apply SDNF in multi-agent epistemic planning. Finally, we extend these results to three more complex multi-agent logics Dn, K45n and KD45n.

scholarly journals Introducing k-lingo: a k-depth Bounded Version of ASP System Clingo

2021 ◽  
Author(s):  
Fabio Aurelio D'Asaro ◽  
Paolo Baldi ◽  
Giuseppe Primiero
Keyword(s):  
Computational Complexity ◽  
Propositional Logic ◽  
Multi Agent Systems ◽  
Classical Propositional Logic ◽  
Rational Agents ◽  
Agent Systems ◽  
Multi Agent ◽  
Future Work ◽  
Limited Depth

Depth-Bounded Boolean Logics (DBBL for short) are well-understood frameworks to model rational agents equipped with limited deductive capabilities. These Logics use a parameter k>=0 to limit the amount of virtual information, i.e., the information that the agent may temporarily assume throughout the deductive process. This restriction brings several advantageous properties over classical Propositional Logic, including polynomial decision procedures for deducibility and refutability. Inspired by DBBL, we propose a limited-depth version of the popular ASP system \clingo, tentatively dubbed k-lingo after the bound k on virtual information. We illustrate the connection between DBBL and ASP through examples involving both proof-theoretical and implementative aspects. The paper concludes with some comments on future work, which include a computational complexity characterization of the system, applications to multi-agent systems and feasible approximations of probability functions.

scholarly journals On Reasoning about Access to Knowledge

10.29007/z15j ◽  
2020 ◽  
Author(s):  
Yakoub Salhi
Keyword(s):  
Crucial Role ◽  
Propositional Logic ◽  
Basic Problem ◽  
Multi Agent Systems ◽  
Classical Propositional Logic ◽  
Inference Process ◽  
Agent Systems ◽  
Multi Agent ◽  
Classical Inference ◽  
Access To Knowledge

Controlling access to knowledge plays a crucial role in many multi-agent systems. In- deed, it is related to different central aspects in interactions among agents such as privacy, security, and cooperation. In this paper, we propose a framework for dealing with access to knowledge that is based on the inference process in classical propositional logic: an agent has access to every piece of knowledge that can be derived from the available knowledge using the classical inference process. We first introduce a basic problem in which an agent has to hide pieces of knowledge, and we show that this problem can be solved through the computation of maximal consistent subsets. In the same way, we also propose a coun- terpart of the previous problem in which an agent has to share pieces of knowledge, and we show that this problem can be solved through the computation of minimal inconsis- tent subsets. Then, we propose a generalization of the previous problem where an agent has to share pieces of knowledge and hide at the same time others. In this context, we introduce several concepts that allow capturing interesting aspects. Finally, we propose a weight-based approach by associating integers with the pieces of knowledge that have to be shared or hidden.

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 A utility-based analysis of equilibria in multi-objective normal-form games

2020 ◽  
Vol 35 ◽  
Author(s):  
Roxana Rădulescu ◽  
Patrick Mannion ◽  
Yijie Zhang ◽  
Diederik M. Roijers ◽  
Ann Nowé
Keyword(s):  
Normal Form ◽  
Optimization Criterion ◽  
Expected Returns ◽  
Objective Functions ◽  
Multi Agent Systems ◽  
Agent Systems ◽  
Normal Form Games ◽  
Multi Objective ◽  
Trade Offs ◽  
Multi Agent

Abstract In multi-objective multi-agent systems (MOMASs), agents explicitly consider the possible trade-offs between conflicting objective functions. We argue that compromises between competing objectives in MOMAS should be analyzed on the basis of the utility that these compromises have for the users of a system, where an agent’s utility function maps their payoff vectors to scalar utility values. This utility-based approach naturally leads to two different optimization criteria for agents in a MOMAS: expected scalarized returns (ESRs) and scalarized expected returns (SERs). In this article, we explore the differences between these two criteria using the framework of multi-objective normal-form games (MONFGs). We demonstrate that the choice of optimization criterion (ESR or SER) can radically alter the set of equilibria in a MONFG when nonlinear utility functions are used.

Design of an Intelligent System to Support Tutors in Learning Communities Using Multi-Agent Systems and Fuzzy Logic

2015 ◽  
Vol 10 (8) ◽  
pp. 845 ◽  
Author(s):  
Youness Chaabi ◽  
R. Messoussi ◽  
V. Hilaire ◽  
Y. Ruichek ◽  
K. Lekdioui ◽  
...  
Keyword(s):  
Fuzzy Logic ◽  
Learning Communities ◽  
Intelligent System ◽  
Multi Agent Systems ◽  
Agent Systems ◽  
Multi Agent
Sign in / Sign up

Export Citation Format

Share Document