How does a boundedly rational optimizing agent make decisions? Can such an agent learn to behave rationally? We address these questions in a standard regulator environment. Our behavioral primitive is anchored to the shadow price of the state vector. The regulator forecasts the value of an additional unit of the state tomorrow, and uses this forecast to choose her control. The value of the cont...

B ulk petrochemical manufacturing is a highly competitive global industry. When margins are tight, manufacturers seek ways to optimize performance and to reduce costs while maximizing yields and revenue. Optimization options include alternative feeds, plant/process revamps and improved operations to achieve better separation and yields to lower energy consumption, to minimize product loss and t...

Many real-world analytics problems involve two significant challenges: prediction and optimization. Because of the typically complex nature each challenge, standard paradigm is predict-then-optimize. By large, machine learning tools are intended to minimize error do not account for how predictions will be used in downstream optimization problem. In contrast, we propose a new very general framew...

Counterfactual quantum communication is unique in its own way that allows remote parties to transfer information without sending any message carrier the channel. Although no travels channel at time of successful transmission, it impossible transmit faster than speed light, thus an carrier. In this paper, we address important question What carries counterfactual communication? and optimize resou...

Communication processors are processors with specific optimizations to support communication systems. Communication processors exist in a wide variety of forms and can be categorized based on the communication system, such as wired or wireless and based on the layer in the communication system, such as the physical layer, the medium access control layer or the network layer. Communication proce...

Agents, in a multi agent system, communicate with each other through the process of exchanging messages which is called dialogue. Multi agent organization is generally used to optimize agents’ communications. Holonic organization demonstrates a self-similar recursive and hierarchical structure in which each holon may include some other holons. In a holonic system, lateral communication occurs b...

Many real-world analytics problems involve two significant challenges: prediction and optimization. Due to the typically complex nature of each challenge, the standard paradigm is to predict, then optimize. By and large, machine learning tools are intended to minimize prediction error and do not account for how the predictions will be used in a downstream optimization problem. In contrast, we p...

Algorithm design is a laborious process and often requires many iterations of ideation and validation. In this paper, we explore automating algorithm design and present a method to learn an optimization algorithm. We approach this problem from a reinforcement learning perspective and represent any particular optimization algorithm as a policy. We learn an optimization algorithm using guided pol...