Answer Set Programming Modulo Theories (ASPMT) is a new framework of tight integration of answer set programming (ASP) and satisfiability modulo theories (SMT). Similar to the relationship between first-order logic and SMT, it is based on a recent proposal of the functional stable model semantics by fixing interpretations of background theories. Analogously to a known relationship between ASP and SA T, "tight" ASPMT programs can be translated into SMT instances. We demonstrate the usefulness of ASPMT by enhancing action language C + to handle continuous changes as well as discrete changes. We reformulate the semantics of C + in terms of ASPMT, and show that SMT solvers can be used to compute the language. We also show how the language can represent cumulative effects on continuous resources.

Enigma, first produced by a German engineer named Arthur Scherbius, is an electro-mechanical encoded communication machine. Its use was evaluated for commercial purposes as it was started after WW1. However, in the aftermath of World War II, Germany was used in military and government services and had great benefits. Enigma models, which are used by Nazi Germany, are much more advanced. Because of its complex encryption systems, it was preferred to send military information.

Planning for hybrid systems is important for dealing with real-world applications, and PDDL+ supports this representation of domains with mixed discrete and continuous dynamics. In this paper we present a new approach for planning for hybrid systems, based on encoding the planning problem as a Satisfiability Modulo Theories (SMT) formula. This is the first SMT encoding that can handle the whole set of PDDL+ features (including processes and events), and is implemented in the planner SMTPlan. SMTPlan not only covers the full semantics of PDDL+, but can also deal with non-linear polynomial continuous change without discretization. This allows it to generate plans with non-linear dynamics that are correct-by-construction. The encoding is based on the notion of happenings, and can be applied on domains with nonlinear continuous change. We describe the encoding in detail and provide in-depth examples. We apply this encoding in an iterative deepening planning algorithm. Experimental results show that the approach dramatically outperforms existing work in finding plans for PDDL+ problems. We also present experiments which explore the performance of the proposed approach on temporal planning problems, showing that the scalability of the approach is limited by the size of the discrete search space. We further extend the encoding to include planning with control parameters. The extended encoding allows the definition of actions to include infinite domain parameters, called control parameters. We present experiments on a set of problems with control parameters to demonstrate the positive effect they provide to the approach of planning via SMT.

David Duvenaud was collaborating on a project involving medical data when he ran up against a major shortcoming in AI. An AI researcher at the University of Toronto, he wanted to build a deep-learning model that would predict a patient's health over time. But data from medical records is kind of messy: throughout your life, you might visit the doctor at different times for different reasons, generating a smattering of measurements at arbitrary intervals. A traditional neural network struggles to handle this. Its design requires it to learn from data with clear stages of observation.

David Duvenaud was working on a project involving medical data when he hit upon a major shortcoming in AI. An AI researcher at the University of Toronto, he wanted to build a deep-learning model that would predict a patient's health over time. But data from medical records is kind of messy: throughout your life, you might visit the doctor at different times for different reasons, generating a smattering of measurements at arbitrary intervals. A traditional neural network struggles to handle this. Its design requires it to learn from data with clear stages of observation.

The ability to model continuous change in Reiter's temporal situation calculus action theories has attracted a lot of interest. In this paper, we propose a new development of his approach, which is directly inspired by hybrid systems in control theory. Specifically, while keeping the foundations of Reiter's axiomatization, we propose an elegant extension of his approach by adding a time argument to all fluents that represent continuous change. Thereby, we insure that change can happen not only because of actions, but also due to the passage of time. We present a systematic methodology to derive, from simple premises, a new group of axioms which specify how continuous fluents change over time within a situation. We study regression for our new temporal basic action theories and demonstrate what reasoning problems can be solved. Finally, we formally show that our temporal basic action theories indeed capture hybrid automata.

There is so much going on that has potential to improve the way that insurers operate, it's hard to know which opportunities to address, and also, which risks are too important to ignore. At SAS, we are fortunate to have a team of industry experts who understand how to contextualise the coming AI opportunities for insurers. This is my summary of various ideas they have published, to help you cut through the hype…. Our recent SASchat discussed the readiness of the insurance ecosystem for artificial intelligence (AI). There was a strong feeling that the insurance industry could really benefit from AI and machine learning, with use cases including claims fraud prevention, and the idea that AI would improve efficiency across the whole process, from underwriting through to claims.

Temporal logics have been used in autonomous planning to represent and reason about temporal planning problems. However, such techniques have typically been restricted to either (1) representing actions, events, and goals with temporal properties or (2) planning for temporally-extended goals under restrictive assumptions. We introduce Mixed Propositional Metric Temporal Logic (MPMTL) where formulae are built over mixed binary and continuous real variables. We introduce a planner, MTP, that solves MPMTL problems and includes a SAT-solver, model checker for a polynomial fragment of MPMTL, and a forward search algorithm. We extend PDDL 2.1 with MPMTL syntax to create MPDDL and an associated parser. The empirical study shows that MTP outperforms the state-of-the-art PDDL+ planner SMTPlan+ on several domains it performed best on and MTP performs and scales on problem size well for challenging domains with rich temporal properties we create.

Robots operating in the real world must be able to handle both discrete and continuous change. Many robot behaviors can be controlled through numeric parameters (called control variables), which affect the rate of the continuous change. Previous approaches capable of reasoning efficiently with control variables impose severe restrictions that limit the expressivity of the problems that can be solved. A broad class of robotic applications require, for example, convex quadratic constraints on state variables and control variables that are jointly constrained and that affect multiple state variables simultaneously. However, extensions to prior approaches are not straightforward, since these characteristics are non-linear and hard to scale. We introduce cqScotty, a heuristic forward search planner that solves these problems efficiently. While naive formulations of consistency checks are not convex and do not scale, cqScotty uses an efficient convex formulation, in the form of a Second Order Cone Program (SOCP), that is very fast to solve. We demonstrate the scalability of our approach on three new realistic domains.

Temporal logics have been used in autonomous planningto represent and reason about temporal planning problems.However, such techniques have typically been restricted toeither (1) representing actions, events, and goals with temporalproperties or (2) planning for temporally-extended goalsunder restrictive conditions of classical planning. We introduceMixed Propositional Metric Temporal Logic (MPMTL),where formulae in MPMTL are built over mixed binary andcontinuous real variables. MPMTL provides a natural, flexibleformalism for representing and reasoning about temporalproblems. We analyze the complexity of MPMTL formulaesatisfiability and model checking, and identify MPMTLfragments with lower complexity. We also introduce an approachto world modeling using a timeline vector, relevant totemporal planning with continuous change (as opposed to theuse of discrete states). Our model supports retroactive actionprogression, concurrent and overlapping actions with discreteand continuous changes, and concurrent effects to the samevariable. For reasoning about this temporal planning problem,we define a progression function for actions with thenew temporal properties and a solution to this temporal task.