A Logic Petri Net Model for Dynamic Multi-Agent Game Decision-Making


This study proposes a logical Petri net model to leverage the modeling advantages of Petri nets in handling batch processing and uncertainty in value passing and to integrate relevant game elements from multi-agent game processes for modeling multi-agent decision problems and resolving optimization issues in dynamic multi-agent game decision-making. Firstly, the attributes of each token are defined as rational agents, and utility function values and state probability transition functions are assigned to them. Secondly, decision transitions are introduced, and the triggering of the optimal decision transition is determined based on a comparison of token utility function values, along with an associated algorithm. Finally, a dynamic game emergency business decision-making process for sudden events is modeled and analyzed using the logic game decision Petri net. 

Based on reachable markings, reachable graphs are constructed to analyze the dynamic game process. Algorithms are described for the generation of reachable graphs, and the paper explores how the logic game decision model for sudden events can address dynamic game decision problems, generate optimal emergency plans, and analyze resource conflicts during emergency processes. The effectiveness and superiority of the model in analyzing the emergency business decision-making process for sudden events are validated. A sudden event is an emergency that poses direct risks and impacts human health, life, and property, requiring urgent intervention to prevent further deterioration. These intervention measures are organized into a process, which is typically described in an emergency plan and referred to as the emergency response process. 

In this process, all emergency personnel are dedicated to managing disasters to minimize or avoid the secondary impacts of the disaster. Generating better contingency plans before emergency responses have become an urgent issue to address. The uncertainty of evacuation time during emergencies, and its stochastic analysis was conducted by coupling the uncertainty of fire detection, alarm, and pre-movement with evacuation time.The forecasting model is event-dependent and takes into account many social and environmental elements regarding different sorts of events, such as socio-economic situations and geographical features. This is due to the great range of emergency occurrences, including both natural and man-made ones. The business decision-making process in disaster operations management varies greatly depending on the type of occurrence, taking into account factors like severity, impacted region, population density, and local environment, among others. 

There are many different types of hazards present worldwide. The health of vulnerable people is placed at risk by natural, biological, technological, and sociological dangers, which also have the potential to seriously impair public health. For instance, the authorities in-charge of providing clean water are responsible for the prevention of waterborne illnesses, while law enforcement and road transportation agencies are in charge of reducing traffic accidents. Zoonotic illnesses (diseases spread from animals to people) need coordinated action from the agricultural, environmental, and health sectors. These increases in new or reemerging diseases are attributed to a number of factors, including global warming, low vaccination rates in high-risk and vulnerable populations, growing vaccine resistance and skepticism, rising anti microbial resistance, and expanding coverage, frequency, and speed of international air travel. A professional who develops plans for emergencies, accidents, and other calamities is known as an emergency management director. Directors of emergency management work together with the leadership team of an organization to evaluate possible hazards and create best practices for handling them. Designing emergency procedures and developing preventative actions to lessen the risk of emergency circumstances occurring fall under their purview. Directors of emergency management play a crucial part in ensuring the safety of all employees and equipping staff to act effectively in case of an emergency. Plans for disaster preparation choose appropriate organizational resources, lay down the tasks and roles, establish rules and processes, and plan exercises to increase preparedness for disasters. The effectiveness of the response activities is improved when the needs of populations affected by catastrophes are anticipated. The effectiveness of the response operations is increased by increasing the ability of workers, volunteers, and disaster management teams to deal with crises. Plans could consist of the following: Sites for temporary refuge, and routes for evacuation water and energy sources for emergencies. Additionally, they might talk about stockpile requirements, communication protocols, training plans, chain of command, and training programs. One of the most crucial metrics for gauging the effectiveness of an evacuation is the time it takes. 

Residents who are detained for an extended period of time represent a serious threat to staff safety because of the unpredictability of events. A building’s inhabitants who attempt to flee during a fire accident exhibit a range of response times (RTs) between the time they are given a warning and the decision to leave. A number of complex factors, such as occupants’ familiarity with evacuation routes, their ability to operate evacuation amenities and fire protection apparatuses, the number of people in the area,and occupants’ psychological and physical conditions and behaviors, can affect how affected personnel are evacuated from a disaster site. Different factors have an impact on evacuation time (ET). The results indicate that it is a variable influenced by a significant number of uncertain factors, including emergency evolution dynamics, human behavior under emergency conditions, and the environment. The benefits of developing appropriate emergency response plans using safety and industrial hygiene resources to mitigate or prevent harm to factory personnel and nearby community residents caused by chlorine gas leaks. Everyone on the team has to be knowledgeable on how to spot leaks and react to them in order to keep the employees safe when handling chlorine. Since chlorine has a strong, unpleasant scent that resembles that of a potent cleaning solution like bleach, most chlorine leaks are quite easy to detect. Every facility that works with chlorine has to have an emergency kit on hand. This kit should include a variety of tools that may be used to stop or limit leaks around plugs, valves, or the side wall of a tank or cylinder used to store chlorine. Breathe in some fresh air and leave the location where the chlorine gas was emitted. If the community has an emergency notification system, be sure they are familiar with it. For directions, consult local authorities and emergency bulletins. If the chlorine discharge occurred outside, seek protection inside. 

To ensure that the contamination does not enter, make sure all windows are closed and ventilation systems are off. Leave the location where the chlorine was discharged if you are unable to get inside. Get outside and look for higher ground if the chlorine discharge occurred indoors. Open the windows and doors to the outdoors if the chlorine leak was caused by chemicals or home cleaners to allow infresh air. We focus on agent-based problem-solving strategies with business decision-making capabilities for CSC, which are based on Multi-criteria business decision-making methods (MCDM) methods for dealing with automated selection in CSC and PN techniques for modeling such context. Petri nets are used as modeling tools in the discrete-event dynamic process known as the multi-agent system. In comparison to alternating current micro grids, direct current micro-grids stand out for their ease of control and power management. They also offer a number of benefits, including higher conversion and transmission efficiency, greater reliability even in re-mote locations, convenient control, lower costs, and less filter effort due to the absence of reactive power, phase synchronization, high inrush current, etc. A rational actor must interact if enhancing subjective utility necessitates interaction with other agents. If there is contact between rational agents, at least one of the agents is trying to maximize his utility. Agents collaborate if their aims are the same. If their aims conflict, they engage in competition. 

The majority of these interactions occur between these two extremes. An interacting agent would do well to predict the objectives of other agents. A more well-informed actor may foresee some aspects of how other agents will act in response to their objectives. In these situations, strategic thinking is required. A contact in which strategic thinking occurs is referred to as a strategic interaction (SI). In game theory, SI or games are examined. The game theory takes into account reason and the potential to forecast rational behavior. The existence of widespread awareness of reason is assumed. This implies that each participant in an interaction believes in there a son of the others and that they, in turn, believe in his rationality, and so on.The equilibrium is the expected behavior of players or participants in an interaction. If one of the players strays from equilibrium, nobody wins. Because of this, it is termed equilibrium. In finite games, there is at least one equilibrium. At least two application agent and mechanism designs are required for artificial intelligence games. We have a game in agent design and must calculate appropriate behavior. We have an expectation about the behavior and must develop game rules in mechanism design. These two goals can be addressed theoretically by running algorithms over a game tree, or practically by creating an environment in which various real players can interact. Most games are written in low-level programming. Game rules are more easily editable. Algorithms may be created that change game representation in every way imaginable, such as ‘reduce number of players’ or ‘remove simultaneous turns’. 

Game representations may also be used to create evolutionary mechanisms. Logical Petri nets can further simplify the network structure of real-time system models, making it easier for us to analyze the properties of the system at a conceptual level, while also alleviating the problem of state space explosion to some extent. Petri nets can not only characterize the structure of a system but also describe its dynamic behavior. Currently, many scholars have proposed extended forms of Petri nets, such as logical Petri nets, timed Petri nets, and colored Petri nets, and their applications are becoming increasingly widespread. Multi-agent games involve multiple elements, such as players, strategies, utilities, and information equilibrium. The existing modeling elements of logical Petri nets cannot accurately describe these elements, so improvements need to be made to logical Petri nets. Based on the existing modeling elements of logical Petri nets, modifications or additions of new modeling elements are needed to model game elements, enabling the new model to accurately describe dynamic game problems in multi-agent systems. 

We consider a mean-field game (MFG)-like scenario where a large number of agents must select between a set of various potential target destinations. This scenario is inspired by effective biological collective decision mechanisms such as the collective navigation of fish schools and honey bees searching for a new colony. The mean trajectory of all agents represents how each person impacts and is impacted by the group’s choice. The model can be seen as a stylized representation of opinion crystallization in a political campaign, for instance. The initial spatial position of the agents determines their biases initially, and then in a later generalization of the model, a combination of starting position and a priori individual preference. The existence criteria for the specified fixed point-based finite population equilibrium conditions are developed. In general, there may be several equilibria, and for the agents to compute them properly, they need to be aware of all the beginning circumstances.

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