Human-Machine Cooperative Decision Making Outperforms Individualism and Autonomy

<div>The experiment reported in this paper provides a first experimental evaluation of human-machine cooperation on decision level: It explicitly focuses on the interaction of human and machine in cooperative decision making situations for which a suitable experimental design is introduced. Furthermore, it challenges conventional leader-follower approaches by comparing them to newly proposed automation designs based on cooperative decision making models. These models originate from negotiation theory and game theory and allow for an investigation of cooperative decision making between equal partners. This equality is motivated by similar approaches on the action level of human-machine cooperation. <br></div><div>The experiment’s results indicate an added value of the proposed automation designs in terms of objective cooperative performance as well as human trust in and satisfaction with the cooperation. Hence, the experiment yields the same insight on decision level as already observed on action level: it may be beneficial to design machines as equal cooperation partners and in accordance to models of emancipated human-machine cooperation.</div>

Human-Machine Cooperative Decision Making Outperforms Individualism and Autonomy

<div>The experiment reported in this paper provides a first experimental evaluation of human-machine cooperation on decision level: It explicitly focuses on the interaction of human and machine in cooperative decision making situations for which a suitable experimental design is introduced. Furthermore, it challenges conventional leader-follower approaches by comparing them to newly proposed automation designs based on cooperative decision making models. These models originate from negotiation theory and game theory and allow for an investigation of cooperative decision making between equal partners. This equality is motivated by similar approaches on the action level of human-machine cooperation. <br></div><div>The experiment’s results indicate an added value of the proposed automation designs in terms of objective cooperative performance as well as human trust in and satisfaction with the cooperation. Hence, the experiment yields the same insight on decision level as already observed on action level: it may be beneficial to design machines as equal cooperation partners and in accordance to models of emancipated human-machine cooperation.</div>

Factors Responsible for Participation of Self Help Group Farmers in Vegetables and Fruits Promotion Council Kerala, India

Self Help Groups (SHGs) are farmer-led cooperatives in which all members work together to solve issues and take advantage of opportunities through participatory action following cooperative decision-making for the members' overall growth. In this context, a study was conducted in Palakkad district of Kerala to identify the factors responsible for the participation of farmer members in SHGs of Vegetables and Fruits Promotion Council Kerala (VFPCK). A proportionate random sampling technique was employed to collect data from 68 respondents and analyzed using mean score then ranked accordingly. The factors like economic, social, personal, organizational and marketing factors may be responsible for members to participate in VFPCK. The results of analysis revealed that marketing, organizational and economic factors were the important factors responsible for the participation of farmer members in VFPCK. Membership to a farmers’ group improves access to technology, training and output markets and consequently increasing expected profits. The results of this study have implications as to which factors need to be addressed to encourage farmers to participate in the SHGs of VFPCK.

Synchronization of Local Plans for Cooperative Distributed Decision-Making

In cooperative decision-making, agents locally plan for a subset of all agents. Due to only local system knowledge of the agents, these local plans may be inconsistent to local plans of other agents. This inconsistency leads to infeasibility of the plans. This article introduces an algorithm for synchronizing local plans for cooperative distributed decision-making of multi-agent systems. The algorithm consists of two iterative steps: planning and synchronization. In the local planning step, the agents compute local decisions, referred to as plans. Subsequently, consistency of the local plans across agents is achieved using synchronization. The synchronized plans act as reference decisions to the local planning step in the next iteration. In each iteration, the local planning guarantees locally feasible plans, while the synchronization guarantees globally consistent plans in that iteration. The algorithm converges to globally feasible decisions if the coupling topology is feasible. We introduce requirements for the coupling topology to achieve convergence to globally feasible decisions and present the algorithm using a model predictive control example. Our evaluations with car-like robots show that feasible decisions are achieved.

Synchronization of Local Plans for Cooperative Distributed Decision-Making

In cooperative decision-making, agents locally plan for a subset of all agents. Due to only local system knowledge of the agents, these local plans may be inconsistent to local plans of other agents. This inconsistency leads to infeasibility of the plans. This article introduces an algorithm for synchronizing local plans for cooperative distributed decision-making of multi-agent systems. The algorithm consists of two iterative steps: planning and synchronization. In the local planning step, the agents compute local decisions, referred to as plans. Subsequently, consistency of the local plans across agents is achieved using synchronization. The synchronized plans act as reference decisions to the local planning step in the next iteration. In each iteration, the local planning guarantees locally feasible plans, while the synchronization guarantees globally consistent plans in that iteration. The algorithm converges to globally feasible decisions if the coupling topology is feasible. We introduce requirements for the coupling topology to achieve convergence to globally feasible decisions and present the algorithm using a model predictive control example. Our evaluations with car-like robots show that feasible decisions are achieved.

Cooperative Target Search of UAV Swarm with Communication Distance Constraint

This paper proposes a cooperative search algorithm to enable swarms of unmanned aerial vehicles (UAVs) to capture moving targets. It is based on prior information and target probability constrained by inter-UAV distance for safety and communication. First, a rasterized environmental cognitive map is created to characterize the task area. Second, based on Bayesian theory, the posterior probability of a target’s existence is updated using UAV detection information. Third, the predicted probability distribution of the dynamic time-sensitive target is obtained by calculating the target transition probability. Fourth, a customized information interaction mechanism switches the interaction strategy and content according to the communication distance to produce cooperative decision-making in the UAV swarm. Finally, rolling-time domain optimization generates interactive information, so interactive behavior and autonomous decision-making among the swarm members are realized. Simulation results showed that the proposed algorithm can effectively complete a cooperative moving-target search when constrained by communication distance yet still cooperate effectively in unexpected situations such as a fire.

The prefrontal cortex and (uniquely) human cooperation: a comparative perspective

Humans have an exceptional ability to cooperate relative to many other species. We review the neural mechanisms supporting human cooperation, focusing on the prefrontal cortex. One key feature of human social life is the prevalence of cooperative norms that guide social behavior and prescribe punishment for noncompliance. Taking a comparative approach, we consider shared and unique aspects of cooperative behaviors in humans relative to nonhuman primates, as well as divergences in brain structure that might support uniquely human aspects of cooperation. We highlight a medial prefrontal network common to nonhuman primates and humans supporting a foundational process in cooperative decision-making: valuing outcomes for oneself and others. This medial prefrontal network interacts with lateral prefrontal areas that are thought to represent cooperative norms and modulate value representations to guide behavior appropriate to the local social context. Finally, we propose that more recently evolved anterior regions of prefrontal cortex play a role in arbitrating between cooperative norms across social contexts, and suggest how future research might fruitfully examine the neural basis of norm arbitration.