Formalization of Weak Emergence in Multiagent Systems

Multiagent Systems代写 In this context, this paper describes the concept of formalization of weak emergence in multi-agent systems.

Venkata Sai Rohan Illindra

Department of Computer Science

University of Adelaide

a1776428@student.adelaide.edu.au

ABSTRACT

Modern systems are complex and composed of parts that are autonomous in their interactions. Due to several agents and interactions in a system, emergent behavior can be exhibited as either desirable or undesirable. As such, formalization is needed to identify the weak emergence in multi-systems. Formalization is critical to evaluate each agent behavior and how they are affecting the overall results of the whole system. Simulation is considered one of the most effective approaches to getting emergence from a system. In this context, this paper describes the concept of formalization of weak emergence in multi-agent systems.

It begins with a brief introduction which explains the motivation of the research. It then continues to explore the related works in the field of emergence in multi-agent systems. The following part critically analyzes the article by Zsabo and Meng Teo. The paper ends with the application of the concept of emergence in a games model. The researchers concluded that emergence in multi-agent systems is a fundamental field that tries to solve undesirable results in a complex system like AI, and hence, developers need to use to verify systems.

KEYWORDS  Multiagent Systems代写

Multi-agent, emergent behavior, complex systems, simulation

ACM Reference Format:

Sai Rohan IV, In Research Essay for Modelling and Analysis of Complex System Assignment

1.Introduction  Multiagent Systems代写

Traditionally, in system development, designers must have basic knowledge and purpose of the system as well as any other possible situation that may affect the system in the future (Oinas-Kukkonen, and Harjumaa, 2018). The systems have little or no autonomy in operations because of future uncertainties (Floreano and Wood, 2015). As such, there is ever-grown system development due to complexities in systems in the bid to control the emergence of unintended usage or misuse. In computer science, it has motivated an increase in various techniques in software development.

Therefore, modern systems have many components that exhibit complex interplays and interconnections. Each of these agents is autonomous based on their internal logic. The complexity exists because of agent independence and interdependence (Nielsen, Larsen, Fitzgerald, Woodcock, and Peleska, 2015). Each of the agents in a system forms the component that is a functional unit. When a system is made, initially the developer has intended purpose. However, at the time of the system usage through the interaction of various agents, there might appear some issues in use or misuse by the agents, which were not intentioned by the developer.

1.1 Motivation  Multiagent Systems代写

When a human can use any deceptive mechanism to take advantage over others. They use pretense in performing a particular action or pretend not to know so as not to share. This is the aspect that is exhibited by autonomous agents in multi-agent systems. That the agents can have hidden actions, resources, or show decoy action to deceive. The paper will focus on evaluating agents and deception emergence in a game system. As such, there is a need to assess emergence in a traffic jam as various autonomous agents interact to get the solution.

1.2 Related Work  Multiagent Systems代写

Relationships and interactions between agents cause emergence. Multi-agents are components of a complex system which act dependently and independently Mohamed, Chebbi, and Behera, 2017).  Emergence is unexpected behavior in systems which might be negative or positive. Each agent is autonomous and has particular action and hence affects how they interact in a system. The aggregate interaction between multi-agents may cause emergence in the system.

Although emergence elicits significant concern from developers, there is no definite approach in evaluating the existence of emergence in the system. Szabo and Meng Tao well explore emergence in systems in their article “Formalization of Weak Emergence in Multiagent System.” The report states that when the number of components, interactions, and connections increase, the level of system complexity also increases.

Thus, the article view emergence from two points of views, including scientific and philosophical (Szabo and Meng Tao, 2015). The distinction between the two depends on the perspective on understanding the behavior of the component where the philosophical emergence is subjective system behavior as opposed to the scientific definition. Hence, to identify emergence, a researcher can use variable based, event-based, and grammar-based approaches.

1.3 History of the Concept  Multiagent Systems代写

The concept of emergence in multi-agents is not new. It was introduced in ancient Greeks where it was termed as “the whole before the parts” (Fan, Chen, and Sun, 2017). The Greeks believed that properties that do not arise from the addition of the behaviors of each component. Emergence is mainly found in complex systems. There is no generally accepted definition of the term emergence in complex systems.

In modern systems, emergence has gained much research owing complex systems in computer science and software engineering. Developers know the software and the intended use and application. However, there are situations that the software system may not function by the developer’s specifications in each of the components. Therefore, emergence has caused systems developments to be continuous to seal gaps likely to cause emergence.

2.Background and Overview  Multiagent Systems代写

In this case, we consider the game and game encounter. In a competitive game, players compete against each other, which is interaction. The game competition is carried out by the defined rules, which is the strategy. The environment of the game defines what action each player can take (D’Addona, Ullah, and Teti, 2017). Therefore, when agents take a particular action simultaneously, their actions are dependent on a combination of actions.

The cumulative set of actions performed by all agents influence the environment to change. The phenomena of changing the environment as a result of certain cumulative actions by agents raise the question of how agents can influence the environment if they all want to maximize their utility. The appropriate action to take is dependent on the goal and the understanding of the actions that lead to emergent behavior. In this case, the agents have negotiated to achieve a position that is favorable to them.

Multiagent Systems代写
Multiagent Systems代写

From the game above, agent I and agent J have the goal of gi and gj, respectively.  Each of the agents can perform or fail to act A and B, as shown below.

1
1
2
2

2.1 System formalism  Multiagent Systems代写

The system can be formalized about the actions of the two agents. Agent i can choose to perform action B, which will give a greater value. Hence, the action for agent i with high utility is dependent on action by agent B. However, agent j can hide his action but inform agent i will take action B, agent i may take action B that is delivering highest utility. Then, agent, i take action A, which gives utility zero for agent i. Hence,

Utility agents = (action A, action B)

2.2 Proposed Process for Emergence Identification  Multiagent Systems代写

The system will be simulated repeatedly identify the emergence. Each of the agents will act independently and will be identified as i and j. Lsum and Lpart will be calculated from action A and B, and the divergence in results over time will be Le (emergence).

3.Related Work  Multiagent Systems代写

3.1 Emergence 

According to Szabo and Meng Tao (2015), emergence is defined in terms of science and philosophy. Philosophically, emergence is a subjective “unexpected behavior in complex systems, the limitation of the observer’s knowledge, the tool employed, and the scale and level of abstraction under which the system is observed. On the other hand, scientific perspective view emergence as intrinsic to the system and an independent view of the system. Thus, emergence is defined as irreducible properties of the whole system, which are associated with components that aggregate to form a system.

Also, Burmaoglu, Sartenaer, and Porter (2019) defined emergence can be defined as the behavior in the process and during the reorganization process of a complex system. Moreover, the phenomena have been defined as the appearance of novelty as well as something unpredictable, unexplainable, and cannot be described in its basic physical terms (Guay and Sartenaer, (2016). Overall, emergence phenomena are considered as the pattern in the results and identifiable in their rights in a complex system. Though identifiable with a system, it is neither predictable nor analyzable from the system.

3.2 Multi-Agent Systems  Multiagent Systems代写

Agents are autonomous components of a system and are adaptable to the environmental changes in the system (Logenthiran, Naayagi, Woo, Phan, and Abidi, 2015). A multi-agent system is made of various agents functioning as an independent component of the whole. Thus, a complex system constitutes agents that exist at the same time, share a resource, and interact with each other. In this context, a multi-agent system can be formalized in the interaction between agents.

3.3 Inter-Relatedness Between the Concepts  Multiagent Systems代写

Emergence is as a result of interaction between the various agents in the system (Lowe, Wu, Tamar, Harb, Abbeel, and Mordatch, 2017). Each agent acts in autonomy to achieve a particular result in the system. The results are considered as the aggregate of all agents’ actions which are controlled by the environment. At the time of interaction, the actions of all agents can influence the results of the whole system (Paez-Perez, and Sanchez-Silva, 2016). That is, even though the agents’ actions are within the set environment, their actions may cause undesired results from the system. It can be explained by Lwhole – Lpart = Le, where the whole is the complex system, and the part is the action of each component in the system.

The agent to agent action in a particular environment determines the whole result system, which might be desirable or undesirable according to the system specifications. The article by Szaba and Meng Teo (2015) use a flock of bird model, which is characterized by various states as birds can be close to each other and form clusters of varying degrees. According to them, the results of the whole is dependent on the complexity of the agent’s interactions, which is determined by the set of rules in the system. The interaction applies some rule and ignores others regarding agents’ interests.

4.Proposed Work  Multiagent Systems代写

4.1 Emergence Formalism

4.1.1 Varieties and Levels of Emergence

Emergence occurs at various levels, including weak and strong (Pariès, 2017). To understand whether emergence has occurred requires simulation of the system at both macro and micro levels. Strong emergence cannot be deduced even in principle following the low-level domain of the system while weak emergence is only unexpected, given the properties and principles of the low-level domain of the system (Wilson, 2015).

A nominal level was introduced to show that a macro property in a system cannot exist in the micro properties. A strong emergence exists a philosophical perspective of the emergence and is strongly emergent concerning a low-level domain. Weak emergence exists in the scientific view of emergent emerge weakly concerning a low-level domain. Strong emergent has strong effects than weak emergence.

Therefore, in defining the strong and weak emergence require modeling the system in a multi-level hierarchy where rule and laws guide the interactions between agents (Wilson, 2015). Reason being, the interactions that lead to emergence are associated with the multi-level hierarchy. The whole system is referred to as the micro-level and the parts or agents that interact statically or dynamically at the lower level are referred to micro-level. The sub-systems at the micro-level can be viewed as systems on their own when looked at the causation factor and the shift between the various levels in the system. From the micro-level, the whole encapsulates the parts during interactions, as shown below.

Multiagent Systems代写
Multiagent Systems代写
4.1.2 Characteristics of Emergence  Multiagent Systems代写

Characterization on whether the change is emergent or not requires abstractions at the two levels of the system hierarchy (Yu, Lv, Ren, Bao, and Hao, 2015). That is the macro and micro level. The whole system is represented as Lwhole, while the aggregate of the agents in the system is presented as Lsum. Thus, system emergence state is the difference between the Lwhole – Lsum, which gives Le. Lwhole is the behavior of the system when the parts are not interacting, and Lsum is the sum of the parts’ behavior without interaction.

The system states are then measured when each agent n interactions are taken in aggregate and measured against the whole state of the system regarding the agent’s interactions. Through simulation, all possible states of the system are calculated for possible system states and to compare the state differences to get emergence. Emergence in a system is characterized by state-space and degree of interactions of agents in micro-level.

4.1.3 Managing Emergence Behaviors  Multiagent Systems代写

Although defining emergence of a complex system is critical, there exist underlying issues (Wall, 2018). First, finding the sum of all the individual agents’ behavior is challenging. Secondly, simulating the system to determine the emergence is time-consuming as it involves repetitive practice and abstraction of agents. Besides, a researcher is faced with forwarding and inverse problems. When the emergence is identified, the challenge is to find the cause depending on whether it is weak or strong emergence.

On the hand, emergence has been a significant concern in artificial intelligence. The concepts of self-organization where agents in the system learn through action can result in emergence of unintended learned behavior in the AI system. The phenomena of undesirable emergence have led to caution in the development of intelligent systems. As a control measure to emergence of undesirable behavior, developers have to carry out rigorous simulation of the system to test the probability of emergence particularly the strong emergence which might not be deductive by principle in the system.

5.Case Study Analysis  Multiagent Systems代写

5.1 Concept Explanation When Modelled in a Multi-Agent System

The case uses a flock of bird model to explore emergence in a multi-agent system. The modeling uses the bottom-up approach to explain the flocking phenomena. In this case, the analysis begins with the behavior of each bird, which has a cumulative effect on the whole system of birds.

5.2 Rules and Behaviors to Note  

The flock of birds uses behavior exhibited when a group of birds is foraging. The bird model has three rules, which include separation, alignment, and cohesion. Separation is for the birds to avoid crowding, alignment means that the birds steer towards the direction of the neighbor, and cohesion is steering towards the average position of neighbor.

6.Conclusion  Multiagent Systems代写

Agent-based systems are complex and need thorough knowledge to predict emergence at development and execution. There are various methods of formalizing emergence, but the most appropriate one is the grammar system. Emergence is affected by the number of interactions made by the agents in the system. Thus, the higher the interaction, the higher the possibility of emergence to occur in a system.

Acknowledgment

The authors wish to thank Luong Ba Linh for discussions about this work.

REFERENCES  Multiagent Systems代写

BURMAOGLU, S., SARTENAER, O., AND PORTER, A. 2019. The conceptual definition of technology emergence: A long journey from the philosophy of science to science policy. Technology in Society.

D’ADDONA, D. M., ULLAH, A. S., AND TETI, R. 2017. Emergent methodology for solving tool inventory sizing problems in a complex production system. Procedia CIRP, 62, 111-116.

FLOREANO, D., AND WOOD, R. J., 2015. Science, technology, and the future of small autonomous drones. Nature, 521(7553), 460.

FAN, S., CHEN, X., AND SUN, Q. 2017. Emergent research of employee safety awareness based on the multi-agent model. In International Conference on Applied Human Factors and Ergonomics (pp. 93-103). Springer, Cham.

GUAY, A., AND SARTENAER, O., 2016. A new look at emergence. Or when after is different. European Journal for Philosophy of Science, 6(2), 297-322.

LOGENTHIRAN, T., NAAYAGI, R. T., WOO, W. L., PHAN, V. T., AND ABIDI, K. 2015. Intelligent control system for microgrids using the multiagent system. IEEE Journal of Emerging and Selected Topics in Power Electronics, 3(4), 1036-1045.

LOWE, R., WU, Y., TAMAR, A., HARB, J., ABBEEL, O. P., AND MORDATCH, I. 2017. Multi-agent actor-critic for mixed cooperative-competitive environments. In Advances in Neural Information Processing Systems (pp. 6379-6390).

MOHAMED, S., CHEBBI, M., AND BEHERA, S. K., 2017. AMMAS: ambient mobile multi-agent system: simulation of the M-learning. International Journal of Modern Education and Computer Science, 9(1), 36.

And

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Multiagent Systems代写

NIELSEN, C. B., LARSEN, P. G., FITZGERALD, J., WOODCOCK, J., AND PELESKA, J. 2015. Systems of systems engineering: basic concepts, model-based techniques, and research directions. ACM Computing Surveys (CSUR), 48(2), 18.

OINAS-KUKKONEN, H., AND HARJUMAA, M., 2018. Persuasive systems design: key issues, process model, and system features. In Routledge Handbook of Policy Design (pp. 105-123). Routledge.

PAEZ-PEREZ, D., AND SANCHEZ-SILVA, M., 2016. A dynamic principal-agent framework for modeling the performance of infrastructure. European Journal of Operational Research, 254(2), 576-594.

PARIÈS, J. 2017. Complexity, emergence, resilience…. In Resilience Engineering (pp. 43-53). CRC Press.

SZABO, C., AND TEO, Y. M. 2015. Formalization of weak emergence in multiagent systems. ACM Transactions on Modeling and Computer Simulation (TOMACS), 26(1), 6.

WILSON, J., 2015. Metaphysical emergence: Weak and strong. Metaphysics in contemporary physics, 251-306.

WALL, F., 2018. The emergence of task formation in organizations: Balancing units’ competence and capacity. Journal of Artificial Societies and Social Simulation, 21(2).

YU, C., LV, H., REN, F., BAO, H., AND HAO, J. 2015. Hierarchical learning for the emergence of social norms in networked multi-agent systems. In Australasian Joint Conference on Artificial Intelligence (pp. 630-643). Springer, Cham.

 

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