This paper introduces a novel model for online dynamic pricing of electric vehicle charging services that integrates reservation, parking, and charging into a comprehensive bundle priced as a whole. Our approach focuses on the individual high-demand, fast-charging location, employing a Poisson process as a model of charging reservation arrivals, and develops an online dynamic pricing strategy optimized through a Markov Decision Process (MDP). A key contribution is the novel analysis of discretization error introduced when incorporating the continuous-time Poisson process into the discrete MDP framework. The MDP model’s feasibility is demonstrated with a heuristic dynamic pricing method based on Monte-Carlo tree search, offering a viable path for real-world applications.

In imitation learning for planning, parameters of heuristic functions are optimized against a set of solved problem instances. This work revisits the necessary and sufficient conditions of strictly optimally efficient heuristics for forward search algorithms, mainly A* and greedy best-first search, which expand only states on the returned optimal path. It then proposes a family of loss functions based on ranking tailored for a given variant of the forward search algorithm. Furthermore, from a learning theory point of view, it discusses why optimizing cost-to-goal h* is unnecessarily difficult. The experimental comparison on a diverse set of problems unequivocally supports the derived theory.

Mutex groups are defined in the context of STRIPS planning as sets of facts out of which, maximally, one can be true in any state reachable from the initial state. This work provides a complexity analysis showing that inference of mutex groups is as hard as planning itself (PSPACE-Complete) and it also shows a tight relationship between mutex groups and graph cliques. Furthermore, we propose a new type of mutex group called a fact-alternating mutex group (fam-group) of which inference is NP-Complete. We introduce an algorithm for the inference of fam-groups based on integer linear programming that is complete with respect to the maximal fam-groups and we demonstrate that fam-groups can be beneficial in the translation of planning tasks into finite domain representation, for the detection of dead-end state and for the pruning of spurious operators. The experimental evaluation of the pruning algorithm shows a substantial increase in a number of solved tasks in domains from the optimal deterministic track of the last two planning competitions (IPC 2011 and 2014).

Mutex groups are defined in the context of STRIPS planning as sets of facts out of which, maximally, one can be true in any state reachable from the initial state. The importance of computing and exploiting mutex groups was repeatedly pointed out in many studies. However, the theoretical analysis of mutex groups is sparse in current literature. This work provides a complexity analysis showing that inference of mutex groups is as hard as planning itself (PSPACE-Complete) and it also shows a tight relationship between mutex groups and graph cliques. This result motivates us to propose a new type of mutex group called a fact-alternating mutex group (fam-group) of which inference is NP-Complete. Moreover, we introduce an algorithm for the inference of fam-groups based on integer linear programming that is complete with respect to the maximal fam-groups and we demonstrate how beneficial fam-groups can be in the translation of planning tasks into finite domain representation. Finally, we show that fam-groups can be used for the detection of dead-end states and we propose a simple algorithm for the pruning of operators and facts as a preprocessing step that takes advantage of the properties of fam-groups. The experimental evaluation of the pruning algorithm shows a substantial increase in a number of solved tasks in domains from the optimal deterministic track of the last two planning competitions (IPC 2011 and 2014).

Multi-agent planning using MA-STRIPS–related models is often motivated by the preservation of private information. Such a motivation is not only natural for multi-agent systems but also is one of the main reasons multi-agent planning problems cannot be solved with a centralized approach. Although the motivation is common in the literature, the formal treatment of privacy is often missing. In this article, we expand on a privacy measure based on information leakage introduced in previous work, where the leaked information is measured in terms of transition systems represented by the public part of the problem with regard to the information obtained during the planning process. Moreover, we present a general approach to computing privacy leakage of search-based multi-agent planners by utilizing search-tree reconstruction and classification of leaked superfluous information about the applicability of actions. Finally, we present an analysis of the privacy leakage of two well-known algorithms—multi-agent forward search (MAFS) and Secure-MAFS—both in general and on a particular example. The results of the analysis show that Secure-MAFS leaks less information than MAFS.

As a part of the workshop on Distributed and Multiagent Planning (DMAP) at the International Conference on Automated Planning and Scheduling (ICAPS) 2015, we have organized a competition in distributed and multiagent planning. The main aims of the competition were to consolidate the planners in terms of input format; to promote development of multiagent planners both inside and outside of the multiagent research community; and to provide a proof-of-concept of a potential future multiagent planning track of the International Planning Competition (IPC). In this paper we summarize course and highlights of the competition.

Multi-agent planning (MAP) is often motivated by the preservation of private information. Such motivation is not only natural for multi-agent systems, but is one of the main reasons, why MAP problems cannot be solved centrally.

Problems of cooperative multiagent planning in deterministic environments can be efficiently solved both by distributed search or coordination of local plans. In the current coordination approaches, behavior of other agents is modeled as public external projections of their actions. The agent does not require any additional information from the other agents, that is the planning process ignores any dependencies of the projected actions possibly caused by sequences of other agents’ private actions. In this work, we formally define several types of internal dependencies of multiagent planning problems and provide an algorithmic approach how to extract the internally dependent actions during multiagent planning. We show how to take an advantage of the computed dependencies by means of reducing the multiagent planning problems. We experimentally show strong reduction of majority of standard multiagent benchmarks and nearly doubling of solved problems in comparison to a variant of a planner without the reductions. The efficiency of the method is demonstrated by winning in a recent competition of distributed multiagent planners.

Interruptible pure exploration in multi-armed bandits (MABs) is a key component of Monte-Carlo tree search algorithms for sequential decision problems. We introduce Discriminative Bucketing (DB), a novel family of strategies for pure exploration in MABs, which allows for adapting recent advances in non-interruptible strategies to the interruptible setting, while guaranteeing exponential-rate performance improvement over time. Our experimental evaluation demonstrates that the corresponding instances of DB favorably compete both with the currently popular strategies UCB1 and Epsilon-Greedy, as well as with the conservative uniform sampling.

Online planning algorithms are typically a tool of choice for dealing with sequential decision problems in combinatorial search spaces. Many such problems, however, also exhibit combinatorial actions, yet standard planning algorithms do not cope well with this type of “the curse of dimensionality”. Following a recently opened line of related work on combinatorial multi-armed bandit (CMAB) problems, we propose a novel CMAB planning scheme, as well as two specific instances of this scheme, dedicated to exploiting what is called linear side information. Using a representative strategy game as a benchmark, we show that the resulting algorithms very favorably compete with the state-of-the-art.

Deterministic multi-agent planning described by MA-STRIPS formalism requires mixture of coordination and synthesis of local agents' plans. All agents' plans, as sequences of actions, can be implicitly described by an appropriate generative structure. Having all local plans of all participating agents described by such a structure and having a merged process of such structures, we can induce a global multi-agent plan by successive elimination of unfeasible combinations of local agents' plans.

Problems of domain-independent multiagent planning for cooperative agents in deterministic environments can be tackled by a well-known initiator--participants scheme from classical multiagent negotiation protocols. In this work, we use the approach to describe a multiagent extension of the Generate-And-Test principle distributively searching for a coordinated multiagent plan. The generate part uses a novel plan quality estimation technique based on metrics borrowed from the field of diverse planning. The test part builds upon planning with landmarks by compilation to classical planning. Finally, the proposed multiagent planning approach was experimentally analyzed on one newly designed domain and one classical benchmark domain. The results show what combination of plan quality estimation and diversity metrics provide the best planning efficiency

Problem solving and planning in decentralized environments is a key technical challenge in numerous industrial applications, ranging from manufacturing, logistics, virtual enterprizes to multirobotics systems. We present an abstract architecture of a multiagent solver and respective algorithm providing decomposition, task allocation, and task delegation. Various features of the abstract architecture, such as computational complexity or admissibility of the underlying optimization heuristics, are analyzed in the paper. Four instances of the abstract architecture implementations are given to demonstrate the applicability of the abstract solver in a wide variety of real-problem domains.

We present an approach to plan representation in multi-actor scenarios that is suitable for flexible replanning and plan revision purposes in dynamic non-deterministic multi-actor environments. The key idea of the presented approach is in representat