Extensions of Answer Set Programming with language constructs from temporal logics, such as temporal equilibrium logic over finite traces (TELf), provide an expressive computational framework for modeling dynamic applications. In this paper, we study the so-called past-present syntactic subclass, which consists of a set of logic programming rules whose body references to the past and head to the present. Such restriction ensures that the past remains independent of the future, which is the case in most dynamic domains. We extend the definitions of completion and loop formulas to the case of past-present formulas, which allows capturing the temporal stable models of a set of past-present temporal programs by means of an LTLf expression.

Though there's a human sitting inside, a careful observer would spot that the truck is calling all the shots. That's because the vehicle is one of a fleet of five autonomous trucks that a Chinese startup called TuSimple is using to ferry containers around the terminal – the first-time self-driving trucks …

FRAMOS, a specialist in industrial image processing, has announced that it is developing state-of-the-art methods, such as 3D cameras, depth modules, and intelligent algorithms, for the IRiS artificial intelligence project for the reliable classification of packing scenarios and the analysis of container contents by intelligent robots. The IRiS project (Interactive Robotic System for Unloading of Sea Containers), being undertaken by FRAMOS, the Bremen Institute for Production und Logistics, and partners BLG Handelslogistik and Schulz Systemtechnik, is conducting research on the automated unloading of standard 40-foot containers. In the future, intelligent robots will carry out this difficult and predominantly manual task automatically. Germany's Federal Ministry of Transport and Digital Infrastructure (BMVI) is funding the three-year project with 2.2 million euros; and, TÜV Rheinland is on board as sponsor of the project. The majority of all sea containers shipped worldwide are unloaded and discharged in the port itself. These containers, with a capacity of 65 cubic meters (2295 cft) and a payload of 26 metric tons, can hold up to 1,800 parcels weighing up to 35 kg each.

Planning--the ability to synthesize a course of action to achieve desired goals--is an important part of intelligent agency and has thus received significant attention within AI for more than 30 years. Work on efficient planning algorithms still continues to be a hot topic for research in AI and has led to several exciting developments in the past few years. This article provides a tutorial introduction to all the algorithms and approaches to the planning problem in AI. To fulfill this ambitious objective, I introduce a generalized approach to plan synthesis called refinement planning and show that in its various guises, refinement planning subsumes most of the algorithms that have been, or are being, developed. It is hoped that this unifying overview provides the reader with a brand-name-free appreciation of the essential issues in planning.

Fork decoupling is a recent approach to exploiting problem structure in state space search. The problem is assumed to take the form of a fork, where a single (large) center component provides preconditions for several (small) leaf components. The leaves are then conditionally independent in the sense that, given a fixed center path p, the compliant leaf moves - those leaf moves enabled by the preconditions supplied along p - can be scheduled independently for each leaf. Fork-decoupled state space search exploits this through conducting a regular search over center paths, augmented with maintenance of the compliant paths for each leaf individually. We herein show that the same ideas apply to much more general star-topology structures, where leaves may supply preconditions for the center, and actions may affect several leaves simultaneously as long as they also affect the center. Our empirical evaluation in planning, super-imposing star topologies by automatically grouping the state variables into suitable components, shows the merits of the approach.

Markov decision processes capture sequential decision making under uncertainty, where an agent must choose actions so as to optimize long term reward. The paper studies efficient reasoning mechanisms for Relational Markov Decision Processes (RMDP) where world states have an internal relational structure that can be naturally described in terms of objects and relations among them. Two contributions are presented. First, the paper develops First Order Decision Diagrams (FODD), a new compact representation for functions over relational structures, together with a set of operators to combine FODDs, and novel reduction techniques to keep the representation small. Second, the paper shows how FODDs can be used to develop solutions for RMDPs, where reasoning is performed at the abstract level and the resulting optimal policy is independent of domain size (number of objects) or instantiation. In particular, a variant of the value iteration algorithm is developed by using special operations over FODDs, and the algorithm is shown to converge to the optimal policy.

Despite their near dominance, heuristic state search planners still lag behind disjunctive planners in the generation of parallel plans in classical planning. The reason is that directly searching for parallel solutions in state space planners would require the planners to branch on all possible subsets of parallel actions, thus increasing the branching factor exponentially. We present a variant of our heuristic state search planner AltAlt, called AltAltp which generates parallel plans by using greedy online parallelization of partial plans. The greedy approach is significantly informed by the use of novel distance heuristics that AltAltp derives from a graphplan-style planning graph for the problem. While this approach is not guaranteed to provide optimal parallel plans, empirical results show that AltAltp is capable of generating good quality parallel plans at a fraction of the cost incurred by the disjunctive planners.

Markov decision processes capture sequential decision making under uncertainty, where an agent must choose actions so as to optimize long term reward. The paper studies efficient reasoning mechanisms for Relational Markov Decision Processes (RMDP) where world states have an internal relational structure that can be naturally described in terms of objects and relations among them. Two contributions are presented. First, the paper develops First Order Decision Diagrams (FODD), a new compact representation for functions over relational structures, together with a set of operators to combine FODDs, and novel reduction techniques to keep the representation small. Second, the paper shows how FODDs can be used to develop solutions for RMDPs, where reasoning is performed at the abstract level and the resulting optimal policy is independent of domain size (number of objects) or instantiation. In particular, a variant of the value iteration algorithm is developed by using special operations over FODDs, and the algorithm is shown to converge to the optimal policy.

Despite their near dominance, heuristic state search planners still lag behind disjunctive planners in the generation of parallel plans in classical planning. The reason is that directly searching for parallel solutions in state space planners would require the planners to branch on all possible subsets of parallel actions, thus increasing the branching factor exponentially. We present a variant of our heuristic state search planner AltAlt, called AltAltp which generates parallel plans by using greedy online parallelization of partial plans. The greedy approach is significantly informed by the use of novel distance heuristics that AltAltp derives from a graphplan-style planning graph for the problem. While this approach is not guaranteed to provide optimal parallel plans, empirical results show that AltAltp is capable of generating good quality parallel plans at a fraction of the cost incurred by the disjunctive planners.

Planning -- the ability to synthesize a course of action to achieve desired goals -- is an important part of intelligent agency and has thus received significant attention within AI for more than 30 years. Work on efficient planning algorithms still continues to be a hot topic for research in AI and has led to several exciting developments i the past few years. This article provides a tutorial introduction to all the algorithms and approaches to the planning problem in AI. To fulfill this ambitious objective, I introduce a generalized approach to plan synthesis called refinement planning and show that in its various guises, refinement planning subsumes most of the algorithms that have been, or are being, developed. It is hoped that this unifying overview provides the reader with a brand-name-free appreciation of the essential issues in planning.