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Collusive bidding has been a deep-seated issue in the construction market for a long time. The strategies implemented by bid riggers are deliberate, interactive, and complex, suggesting that antitrust authorities have difficulty preventing collusive behaviors. Based on game payoff matrixes, this study proposes a system dynamics (SD) model to present the deterrence of punitive measures, namely the certainty of punishment (CoP) and the severity of punishment (SoP), on regular bidders' to-collude decision-making. Data were collected from the Chinese construction industry to test the proposed SD model. While the model was supported, the results indicate that the CoP has a greater impact than the SoP on deterring regular bidders from making to-collude decisions. Furthermore, these two punitive measures cannot be replaced by each other, given the same deterrence effects. Thus, the study demonstrates the usefulness of deterrence theory to inhibit collusive bidding in the construction sector. It also sheds some light on the formulation of competition policy from the perspective of deterrence. [ABSTRACT FROM AUTHOR]

Multiobjective evolutionary algorithm based on decomposition (MOEA/D) is an efficient mathematical strategy for solving multiobjective optimization problems. However, the MOEA/D algorithm has not yet been widely used on the multiobjective optimal power flow (MOPF) problems, which consider several conflicting objectives with varying tradeoff levels. This article proposes a novel differential evolution (DE) strategy based on the MOEA/D framework to quickly determine a set of optimal solutions of MOPFs, in the objective space formed from the different objectives, such as the most optimal economic dispatch, the least environmental emission objectives, and the minimum transmission losses, while considering the power system constraints. A judicious decision can be made by the user from the set of optimal solutions of the MOPF associated with the weight vectors representing the tradeoff levels of the different objectives. For improved performance, two aggregate objective functions, a load flow operator and a self-adaptive DE strategy work cooperatively: 1) to improve the weak convergence of the MOEA/D and to achieve a better decision speed; 2) to obtain more accurate optimal solutions even under non-convex conditions; 3) to ensure that the power system constraints are taken into account; 4) to integrate the above features into a fast and efficient algorithm. The proposed algorithm has been validated using the IEEE 30-bus system and a revised 33-bus radial system added to one node of the 30-bus system. The simulation results show that the proposed algorithm can provide a good accuracy and can converge to a set of optimal solutions of the MOPFs. [ABSTRACT FROM AUTHOR]

Autonomous driving is one of the most important AI applications and has attracted extensive interest in recent years. A large number of studies have successfully applied reinforcement learning techniques in various aspects of autonomous driving, ranging from low-level control of driving maneuvers to higher level of strategic decision-making. However, comparatively less progress has been made in investigating how co-existing autonomous vehicles would interact with each other in a common environment and how reinforcement learning can be helpful in such situations by applying multiagent reinforcement learning techniques in the high-level strategic decision-making of the following or overtaking for a group of autonomous vehicles in highway scenarios. Learning to achieve coordination among vehicles in such situations is challenging due to the unique feature of vehicular mobility, which renders it infeasible to directly apply the existing coordinated learning approaches. To solve this problem, we propose using dynamic coordination graph to model the continuously changing topology during vehicles’ interactions and come up with two basic learning approaches to coordinate the driving maneuvers for a group of vehicles. Several extension mechanisms are then presented to make these approaches workable in a more complex and realistic setting with any number of vehicles. The experimental evaluation has verified the benefits of the proposed coordinated learning approaches, compared with other approaches that learn without coordination or rely on some traditional mobility models based on some expert driving rules. [ABSTRACT FROM AUTHOR]

The primary objective of this study is to compare pedestrian evacuation strategies in the large-scale public space (LPS) using microscopic model. Data were collected by video recording from Tian-yi square for 36 hours in city of Ningbo, China. A pedestrian evacuation simulation model was developed based on the social force model (SFM). The simulation model parameters, such as reaction time, elasticity coefficient, sliding coefficient, et al, were calibrated using the real data extracted from the video. Five evacuation strategies, strategy 1 (S1) to strategy 5 (S5) involving distance, density and capacity factors were simulated and compared by indicators of evacuation time and channel utilization rate, as well as the evacuation efficiency. The simulation model parameters calibration results showed that a) the pedestrians walking speed is 1.0 ~ 1.5m/s; b) the pedestrians walking diameter is 0.3 ~ 0.4m; c) the frequency of pedestrian arrival and departure followed multi-normal distribution. The simulation results showed that, (a) in terms of total evacuation time, the performance of S4 and S5 which considering the capacity and density factors were best in all evacuation scenarios, the performance of S3 which only considering the density factor was the worst, relatively, and S1 and S2 which considering the distance factor were in the middle. (b) the utilization rate of channels under S5 strategy was better than other strategies, which performs best in the balance of evacuation. S3 strategy was the worst, and S1, S2 and S4 were in the middle. (c) in terms of the evacuation efficiency, when the number of evacuees is within 2, 500 peds, the S1 and S2 strategy which considering the distance factor have best evacuation efficiency than other strategies. And when the number of evacuees is above 2, 500 peds, the S4 and S5 strategy which considering the capacity factor are better than others. [ABSTRACT FROM AUTHOR]

Multiagent learning (MAL) is a promising technique for agents to learn efficient coordinated behaviors in multiagent systems (MASs). In MAL, concurrent multiple distributed learning processes can make the learning environment nonstationary for each individual learner. Developing an efficient learning approach to coordinate agents’ behaviors in this dynamic environment is a difficult problem, especially when agents do not know the domain structure and have only local observability of the environment. In this paper, a coordinated MAL approach is proposed to enable agents to learn efficient coordinated behaviors by exploiting agent independence in loosely coupled MASs. The main feature of the proposed approach is to explicitly quantify and dynamically adapt agent independence during learning so that agents can make a trade-off between a single-agent learning process and a coordinated learning process for an efficient decision making. The proposed approach is employed to solve two-robot navigation problems in different scales of domains. Experimental results show that agents using the proposed approach can learn to act in concert or independently in different areas of the environment, which results in great computational savings and near optimal performance. [ABSTRACT FROM PUBLISHER]

This paper presents a hybrid multiagent framework with a Q-learning algorithm to support rapid restoration of power grid systems following catastrophic disturbances involving loss of generators. This framework integrates the advantages of both centralized and decentralized architectures to achieve accurate decision making and quick responses when potential cascading failures are detected in power systems. By using this hybrid framework, which does not rely on a centralized controller, the single point of failure in power grid systems can be avoided. Further, the use of the Q-learning algorithm developed in conjunction with the restorative framework can help the agents to make accurate decisions to protect against cascading failures in a timely manner without requiring a global reward signal. Simulation results demonstrate the effectiveness of the proposed approach in comparison with the typical centralized and decentralized approaches based on several evaluation attributes. [ABSTRACT FROM PUBLISHER]

Agent negotiation is a form of decision making where two or more agents jointly search for a mutually agreed solution to a certain problem. In multi-issue negotiation, with information available about the agents’ preferences, a negotiation may result in a mutually beneficial agreement. In a competitive negotiation environment, however, self-interested agents may not be willing to reveal their preferences, and this can increase the difficulty of negotiating a mutually beneficial agreement. In order to solve this problem, this paper proposes a Bayesian-based approach which can help an agent to predict its opponent’s preference in bilateral multi-issue negotiation. The proposed approach employs Bayesian theory to analyse the opponent’s historical offers and to approximately predict the opponent’s preference over negotiation issues. A counter-offer proposition algorithm is also integrated into the prediction approach to help agents to propose mutually beneficial offers based on the prediction results. Experimental results indicate good performance of the proposed approach in terms of utility gain and negotiation efficiency. [ABSTRACT FROM AUTHOR]