Abstract: Embodiments described herein provide systems and methods for solving and applying a multi-agent decision process. A system performs a process, where at each iterative step, the system determines policies for a plurality of agents that optimize respective reward values based on the plurality of costs, and the characteristics of the plurality of agents. The system simulates the multi-agent decision process using the determined policies, thereby generating respective reward values and aggregated resource contribution values. The system increments or decrements the plurality of costs based on the constraints and the aggregated resource contribution values. The system updates a final reward value based on the respective reward values. The system updates a final plurality of costs based on the plurality of costs. After performing the iterative step for a predetermined number of iterations, the system outputs the final reward value and the final plurality of costs.

Abstract: Embodiments provide a method for predicting agent actions for neural network based agents according to an intervention. The method includes obtaining a first agent action at a first time step and a first intervention generated according to an intervention policy. The method also includes generating, by the neural network based agent model, a predicted agent action conditioned on the first agent action and the first intervention. The method also includes generating, by a neural network based intervention model, a second intervention according to the intervention policy and conditioned on the first agent action, the first intervention, and the predicted agent action. The method further includes executing a second agent action according to an agent policy that incurs a reward based on the second intervention. The method further includes training the neural network based intervention model by updating parameters of the neural network based intervention model based on an expected return.

Abstract: A rational inattention reinforcement learning (RIRL) framework determines actions of actors based on observations while modeling human irrationality or rational inattention. The RIRL framework decomposes observations into a set of observations, and passes the set through multiple information channels modeled as encoders having different information costs. Discriminators of the encoders measure a cost of mutual information (MI) associated with the observations. A stochastic action module of the RIRL framework receives encodings of the encoders and a history of encoded information from a previous iteration, and generates a distribution of actions. The stochastic action module includes a discriminator for measuring a cost of MI associated with the stochastic action module. The RIRL framework computes a reward based on the cost of MI of stochastic encoders, the cost of MI of the stochastic action module, and the distribution of actions. From the reward, the actions of the actors are determined.

Abstract: Approaches for determining a response for an agent in an undirected dialogue are provided. The approaches include a dialogue generating framework comprising an encoder neural network, a decoder neural network, and a language model neural network. The dialogue generating framework generates a sketch sentence response with at least one slot. The sketch sentence response is generated word by word and takes into account the undirected dialogue and agent traits of the agent making the response. The dialogue generating framework generates sentence responses by filling the slot with words from the agent traits. The dialogue generating framework ranks the sentence responses according to perplexity by passing the sentence responses through a language model and selects a final response which is a sentence response that has a lowest perplexity.

Abstract: Approaches for determining a response for an agent in an undirected dialogue are provided. The approaches include a dialogue generating framework comprising an encoder neural network, a decoder neural network, and a language model neural network. The dialogue generating framework generates a sketch sentence response with at least one slot. The sketch sentence response is generated word by word and takes into account the undirected dialogue and agent traits of the agent making the response. The dialogue generating framework generates sentence responses by filling the slot with words from the agent traits. The dialogue generating framework ranks the sentence responses according to perplexity by passing the sentence responses through a language model and selects a final response which is a sentence response that has a lowest perplexity.