Reasoning In Multi-agent Systems Research Articles

Optimal Task and Motion Planning and Execution for Multiagent Systems in Dynamic Environments.

Combining symbolic and geometric reasoning in multiagent systems is a challenging task that involves planning, scheduling, and synchronization problems. Existing works overlooked the variability of task duration and geometric feasibility intrinsic to these systems because of the interaction between agents and the environment. We propose a combined task and motion planning approach to optimize the sequencing, assignment, and execution of tasks under temporal and spatial variability. The framework relies on decoupling tasks and actions, where an action is one possible geometric realization of a symbolic task. At the task level, timeline-based planning deals with temporal constraints, duration variability, and synergic assignment of tasks. At the action level, online motion planning plans for the actual movements dealing with environmental changes. We demonstrate the approach's effectiveness in a collaborative manufacturing scenario, in which a robotic arm and a human worker shall assemble a mosaic in the shortest time possible. Compared with existing works, our approach applies to a broader range of applications and reduces the execution time of the process.

Onto-agent-SSSN: an ontology model to facilitate reactive reasoning in multi-agent systems within a business intelligence network

Business intelligence network integrates the notion of collective intelligence to allow groups of individual collaborate collectively to produce useful information for a company on which decisions and strategies are based. However, performance of collective intelligence is linked to humans' involvement and his reflex to exchange expertise and ideas. In this paper, a platform based on a multi agent system called Agent-SSSN was proposed to create collective intelligence in a business intelligence network using agents' ability to reason as human experts guided by semantic web technologies. To organise the knowledge in Agent-SSSN and facilitate the reasoning behaviour of the agents, an ontology called Onto-Agent-SSSN was designed and implemented in Protege 3.4.8 through a case study based on the competitive intelligence of the banking sector. Finally, this research discusses the limitations of the proposed approach and future research directions.

Onto-agent-SSSN: an ontology model to facilitate reactive reasoning in multi-agent systems within a business intelligence network

Onto-agent-SSSN: an ontology model to facilitate reactive reasoning in multi-agent systems within a business intelligence network

Graded modalities in Strategy Logic

Strategy Logic (SL) is a logical formalism for strategic reasoning in multi-agent systems. Its main feature is that it has variables for strategies that are associated to specific agents using a binding operator. In this paper we introduce Graded Strategy Logic (GradedSL), an extension of SL by graded quantifiers over tuples of strategy variables, i.e., “there exist at least g different tuples (x1,...,xn) of strategies” where g is a cardinal from the set N∪{ℵ0,ℵ1,2ℵ0}. We prove that the model-checking problem of GradedSL is decidable. We then turn to the complexity of fragments of GradedSL. When the g's are restricted to finite cardinals, written GradedNSL, the complexity of model-checking is no harder than for SL, i.e., it is non-elementary in the quantifier-block rank. We illustrate our formalism by showing how to count the number of different strategy profiles that are Nash equilibria (NE). By analysing the structure of the specific formulas involved, we conclude that the important problem of checking for the existence of a unique NE can be solved in 2ExpTime, which is not harder than merely checking for the existence of such an equilibrium.

Extended Graded Modalities in Strategy Logic

Strategy Logic (SL) is a logical formalism for strategic reasoning in multi-agent systems. Its main feature is that it has variables for strategies that are associated to specific agents with a binding operator. We introduce Graded Strategy Logic (GradedSL), an extension of SL by graded quantifiers over tuples of strategy variables, i.e., "there exist at least g different tuples (x_1,...,x_n) of strategies" where g is a cardinal from the set N union {aleph_0, aleph_1, 2^aleph_0}. We prove that the model-checking problem of GradedSL is decidable. We then turn to the complexity of fragments of GradedSL. When the g's are restricted to finite cardinals, written GradedNSL, the complexity of model-checking is no harder than for SL, i.e., it is non-elementary in the quantifier rank. We illustrate our formalism by showing how to count the number of different strategy profiles that are Nash equilibria (NE), or subgame-perfect equilibria (SPE). By analyzing the structure of the specific formulas involved, we conclude that the important problems of checking for the existence of a unique NE or SPE can both be solved in 2ExpTime, which is not harder than merely checking for the existence of such equilibria.

A Computational Framework for Practical Social Reasoning

This article describes a framework for practical social reasoning designed to be used for analysis, specification, and implementation of the social layer of agent reasoning in multiagent systems. Our framework, called theexpectation strategy behavior(ESB) framework, is based on (i) using sets of update rules for social beliefs tied to observations (so‐called expectations), (ii) bounding the amount of reasoning to be performed over these rules by defining a reasoningstrategy, and (iii) influencing the agent's decision‐making logic by means ofbehaviorsconditioned on the truth status of current and future social beliefs. We introduce the foundations of ESB conceptually and present a formal framework and an actual implementation of a reasoning engine, which is specifically combined with a general (belief–desire–intention‐based) practical reasoning programming system. We illustrate the generality of ESB through select case studies, which show that it is able to represent and implement different typical styles of social reasoning. The broad coverage of existing social reasoning methods, the modularity that derives from its declarative nature, and its focus on practical implementation make ESB a useful tool for building advanced socially reasoning agents.

Rough Mereology as a Tool for Knowledge Discovery and Reasoning in Intelligent Systems: A Survey

In this work, we present an account of our recent results on applications of rough mereology to problems of 1) knowledge granulation; 2) granular preprocessing in knowledge discovery by means of decision rules; 3) spatial reasoning in multi-agent systems in exemplary case of intelligent mobile robotics.

Accelerated Method Based on Reinforcement Learning and Case Base Reasoning in Multi agent Systems

paper, a new algorithm based on case base reasoning and reinforcement learning is proposed to increase the rate convergence of the reinforcement learning algorithms in multi- agent systems. In the propose method, we investigate how making improved action selection in reinforcement learning (RL) algorithm. In the proposed method, the new combined model using case base reasoning systems and a new optimized function has been proposed to select the action, which has led to an increase in algorithms based on Q-learning. The algorithm mentioned has been used for solving the problem of cooperative Markov's games as one of the models of Markov based multi- agent systems. The results of experiments have shown that the proposed algorithms perform better than the existing algorithms in terms of speed and accuracy of reaching the optimal policy.

A commonsense knowledge base supported multi-agent architecture

As the global communication and knowledge exchange between different domains in multi-agent systems (MAS) becomes more important, domain-specific knowledge can no longer solely support reasoning in MAS, whereas commonsense knowledge becomes more critical to the reasoning quality. Incorporating the commonsense knowledge bases (CKB) is at the cutting edge of this field. We are interested in how to reuse CKB in multi-agent systems architectures. Our target is to identify the best way, in terms of efficiency and effectiveness, for MAS to reason with the support of the commonsense knowledge base. In this study, we analyze the reasons for and feasibility of commonsense knowledge reuse in MAS during the knowledge management process, and investigate the commonsense knowledge base-supported MAS architecture design. Four possible approaches to incorporating commonsense knowledge into MAS, with either homogeneous or heterogeneous data, are proposed and tested with the same scenario. The performances have been evaluated using standardized metrics. After comparing the results, one approach is recommended and justifications are given, which constitutes the main contribution of this paper.

On the computational complexity of qualitative coalitional games

We study coalitional games in which agents are each assumed to have a goal to be achieved, and where the characteristic property of a coalition is a set of choices, with each choice denoting a set of goals that would be achieved if the choice was made. Such qualitative coalitional games ( qcgs) are a natural tool for modelling goal-oriented multiagent systems. After introducing and formally defining qcgs, we systematically formulate fourteen natural decision problems associated with them, and determine the computational complexity of these problems. For example, we formulate a notion of coalitional stability inspired by that of the core from conventional coalitional games, and prove that the problem of showing that the core of a qcg is non-empty is D p 1-complete. (As an aside, we present what we believe is the first “natural” problem that is proven to be complete for D p 2.) We conclude by discussing the relationship of our work to other research on coalitional reasoning in multiagent systems, and present some avenues for future research.

Formalizing Collaborative Decision-making and Practical Reasoning in Multi-agent Systems

In this paper, we present an abstract formal model of decision-making in a social setting that covers all aspects of the process, from recognition of a potential for cooperation t hrough to joint decision. In a multi-agent environment, where self-motivated autonomous agents try to pursue their own goals, a joint decision cannot be taken for granted. In order to decide effectively, agents need the ability to (a) represent and maintain a model of their own mental attitudes, (b) reason about other agents’ mental attitudes, and (c) influence other agents’ mental states. Social mental shaping is advocated as a general mechanism for attempting to have an impact on agents’ mental states in order to increase their cooperativeness towards a joint decision. Our approach is to specify a novel, high-level architecture for collaborative decision-making in which the mentalistic notions of belief, desire, goal, intention, preference and commitment play a central role in guiding the individual agent’s and the group’s decision-making behaviour. We identify preconditions that must be fulfilled before colla borative decision-making can commence and prescribe how cooperating agents should behave, in terms of their own decision-making apparatus and their interactions with others, when the decision-making process is progressing satisfactorily. The model is formalized through a new, many-sorted, multi-modal logic.

On Social Reasoning in Multi-Agent Systems

This work presents the core notions of a social reasoning mechanism, based on dependence theory. This model enables an agent to reason about the others, in particular to calculate his dependence relations and dependence situations. An agent is said to be dependent on another if the latter can help/prevent him to achieve one of his goals. We consider our social reasoning mechanism as an essential building block for the design of really autonomous artificial agents, which are immersed in an open multi-agent world. By open, we mean that agents may enter or leave the society at any moment. In such systems, as the organization of the agents can not be conceived at design time, the cooperative problem solving paradigm is based on dynamic coalition formation. In this context, agents must be able to adapt themselves to dynamically changing conditions, by evaluating at execution time if their goals are achievable and if their plans are feasible. As we do not suppose that agents are benevolent, our model proposes a criterion to evaluate which partners are more susceptible to accept a proposition of coalition. Finally, as in these kind of systems agents usually do not have a complete and correct representation of each other, our model helps them to detect an agency level inconsistency and to choose a context to be maintained.