In planning and reinforcement learning, the identification of common subgoal structures across problems is important when goals are to be achieved over long horizons. Recently, it has been shown that such structures can be expressed as feature-based rules, called sketches, over a number of classical planning domains. These sketches split problems into subproblems which then become solvable in low polynomial time by a greedy sequence of IW$(k)$ searches. Methods for learning sketches using feature pools and min-SAT solvers have been developed, yet they face two key limitations: scalability and expressivity. In this work, we address these limitations by formulating the problem of learning sketch decompositions as a deep reinforcement learning (DRL) task, where general policies are sought in a modified planning problem where the successor states of a state s are defined as those reachable from s through an IW$(k)$ search. The sketch decompositions obtained through this method are experimentally evaluated across various domains, and problems are regarded as solved by the decomposition when the goal is reached through a greedy sequence of IW$(k)$ searches. While our DRL approach for learning sketch decompositions does not yield interpretable sketches in the form of rules, we demonstrate that the resulting decompositions can often be understood in a crisp manner.

According to Destin, Hudack used automation to make Deliveroo's ordering process more efficient. The startup refers to live orders as "orders in flight," and Hudack specifically looked at the number of times Deliveroo has contact with an order, from the moment a customer puts in a request for food through to the moment it's delivered by a driver. Through automation, Deliveroo reduced that number "by 98%" -- making 25 people redundant in the process. March figures from PwC suggest Destin is probably right about automation destroying jobs, though the actual numbers are more conservative.

One approach to bridging the historic divide between "symbolic" and "subsymbolic" AI is to incorporate a subsymbolic system and a symbolic system into a synergetic integrative cognitive architecture. Here we consider various issues related to incorporating (subsymbolic) compositional spatiotemporal deep learning networks (CSDLNs, a term introduced to denote the category including HTM, DeSTIN and other similar systems) into an integrative cognitive architecture including symbolic aspects. The core conclusion is that for such integration to be meaningful, it must involve dynamic and adaptive linkage and conversion between CSDLN attractors spanning sensory, motor and goal hierarchies, and analogous representations in the remainder of the integrative architecture. We suggest the mechanism of "semantic CSDLNs", which maintain the general structure of CSDLNs but contain more abstract patterns, similar to those represented in more explicitly symbolic AI systems. This notion is made concrete by describing a planned integration of the DeSTIN CSDLN into the OpenCog integrative cognitive system (which includes a probabilistic-logical symbolic component).

The topic of deep learning systems has received significant attention during the past few years, particularly as a biologically-inspired approach to processing highdimensional signals. The latter often involve spatiotemporal information that may span large scales, rendering its representation in the general case highly challenging. Deep learning networks attempt to overcome this challenge by means of a hierarchical architecture that is comprised of common circuits with similar (and often cortically influenced) functionality. The goal of such systems is to represent sensory observations in a manner that will later facilitate robust pattern classification, mimicking a key attribute of the mammal brain. This stands in contrast with the mainstream approach of pre-processing the data so as to reduce its dimensionality — a paradigm that often results in sub-optimal performance. This paper presents a Deep SpatioTemporal Inference Network (DeSTIN) — a scalable deep learning architecture that relies on a combination of unsupervised learning and Bayesian inference. Dynamic pattern learning forms an inherent way of capturing complex spatiotemporal dependencies. Simulation results demonstrate the core capabilities of the proposed framework, particularly in the context of high-dimensional signal classification.