Multi-objective search can be used to model many real-world problems that require finding Pareto-optimal paths from a specified start state to a specified goal state, while considering different cost metrics such as distance, time, and fuel. The ...

Many interesting search problems can be formulated as bi-objective search problems, that is, search problems where two kinds of costs have to be minimized, for example, travel distance and time for transportation problems. Instead of ...

Multi-agent pathfinding (MAPF) is an NP-hard problem. As such, dense maps may be very hard to solve optimally. In such scenarios, compilation-based approaches, via Boolean satisfiability (SAT) and answer set programming (ASP), have been shown to ...

In real-time pathfinding in unknown terrain an agent is required to solve a pathfinding problem by alternating a time-bounded deliberation phase with an action execution phase. Real-time heuristic search algorithms are designed for general search ...${\mathrm{}\mathrm{}}_{\mathrm{}}$${\mathrm{}\mathrm{}}_{\mathrm{}}$${\mathrm{}\mathrm{}}_{\mathrm{}}$${\mathrm{}\mathrm{}}_{\mathrm{}}$

Multiplying the heuristic function by a weight greater than one is a well-known technique in Heuristic Search. When applied to A* with an admissible heuristic it yields substantial runtime savings, at the expense of sacrificing solution optimality. Only ...

In this paper, we investigate real-time path planning in static terrain, as needed in video games. We introduce the game time model, where time is partitioned into uniform time intervals, an agent can execute one movement during each time interval, and ...

RTAA* is probably the best-performing real-time heuristic search algorithm at path-finding tasks in which the environment is not known in advance or in which the environment is known and there is no time for pre-processing. As most realtime search ...

Heuristics used for solving hard real-time search problems have regions with depressions. Such regions are bounded areas of the search space in which the heuristic function is exceedingly low compared to the actual cost to reach a solution. Real-time ...

Heuristic depressions are local minima of heuristic functions. While visiting one them, real-time (RT) search algorithms like LRTA* will update the heuristic value for most of their states several times before escaping, resulting in costly solutions. ...

Real-time search is a standard approach to solving search problems in which agents have limited sensing capabilities and must act quickly. It is well known that real-time search algorithms like LRTA* and RTA* perform poorly in regions of the search ...

The FF relaxed plan heuristic is one of the most effective techniques in domain-independent satisficing planning and is used by many state-of-the-art heuristic-search planners. However, it may sometimes provide quite inaccurate information, since its ...

Planning with preferences involves not only finding a plan that achieves the goal, it requires finding a preferred plan that achieves the goal, where preferences over plans are specified as part of the planner's input. In this paper we provide a ...

In this paper we propose a suite of techniques for planning with temporally extended preferences (TEPs). To this end, we propose a method for compiling TEP planning problems into simpler domains containing only final-state (simple) preferences and ...

Temporally extended goals (TEGs) refer to properties that must hold over intermediate and/or final states of a plan. The problem of planning with TEGs is of renewed interest because it is at the core of planning with temporal preferences. Currently, the ...