Multi-agent epistemic planning (MEP) addresses planning problems involving multiple agents with epistemic reasoning, often requiring the consideration of nested beliefs. In this paper, we extend the notion of traps in classical planning to MEP, and call them belief traps, which are epistemic formulas that once entailed by an epistemic state, remain entailed by all successor states. Identifying belief traps can sometimes improve MEP solving significantly. Here, we consider two methods for identifying and using belief traps to improve planning efficiency. Our first method adapts a classical preprocessing algorithm with integration into an MEP planner, simple-form construction of traps, and a novel use of beneficial traps to guide search. The second method systematically generalizes the belief lock strategy by formalizing its underlying preservation condition. Our experiments show that the new pruning techniques can accelerate problem-solving in the domains with irreversible beliefs.

Multi-agent epistemic planning (MEP) is about achieving an epistemic goal in a multi-agent environment using agents’ actions that have epistemic preconditions and effects. Recently, MEP has received interest from both the dynamic logic and planning communities, leading to the development of several innovative planners. One such state of the art planner is MEPK. In this paper, we propose two novel strategies to enhance the search methods within MEPK. Our first strategy, the enhancement strategy, dynamically updates the heuristic based on the search path to the first goal-reachable node, potentially reducing the number of nodes that need to be explored to find a solution. Our second, the belief lock strategy, prevents the planner from continuing to search a particular state that cannot progress to a goal state due to the possession by an agent of a certain belief. Our experiments on existing benchmarks show that the new strategies can indeed accelerate the problem solving. We also construct new harder instances and demonstrate that our strategies significantly improve the performance on these hard benchmarks. Overall, we consider our new planner a significant improvement over the existing one in terms of computational efficiency.

Configuration checking (CC) has been confirmed to alleviate the cycling problem in local search for combinatorial optimization problems (COPs). When using CC heuristics in local search for graph problems, a critical concept is the configuration of the vertices. All existing CC variants employ either 1- or 2-level neighborhoods of a vertex as its configuration. Inspired by the idea that neighborhoods with different levels should have different contributions to solving COPs, we propose the probabilistic configuration (PC), which introduces probabilities for neighborhoods at different levels to consider the impact of neighborhoods of different levels on the CC strategy. Based on the concept of PC, we first propose probabilistic configuration checking (PCC), which can be developed in an automated and lightweight favor. We then apply PCC to two classic COPs which have been shown to achieve good results by using CC, and our preliminary results confirm that PCC improves the existing algorithms because PCC alleviates the cycling problem.

Prime compilation aims to generate all prime implicates/implicants of a Boolean formula. Recently, prime compilation of non-clausal formulae has received great attention. Since it is hard for$\Sigma_{2}^{P}$, existing methods have performance issues. We argue that the main performance bottleneck stems from enlarging the search space using dual rail (DR) encoding, and computing a minimal clausal formula as a by-product. To deal with the issue, we propose a two-phase approach, namely CoAPI, for prime compilation of non-clausal formulae. Thanks to the two-phase framework, we construct a clausal formula without using DR encoding. In addition, to improve performance, the key in our work is a novel bounded prime extraction (BPE) method that, interleaving extracting prime implicates with extracting small implicates, enables constructing a succinct clausal formula rather than a minimal one. Following the assessment way of the state-of-the-art (SOTA) work, we show that CoAPI achieves SOTA performance. Particularly, for generating all prime implicates, CoAPI is up to about one order of magnitude faster. Moreover, we evaluate CoAPI on a benchmark sourcing from real-world industries. The results also confirm the outperformance of CoAPI11Our code and benchmarks are publicly available at https://github.com/LuoWeiLinWillam/CoAPI.

In goal-oriented requirements engineering, goal conflict identification is of fundamental importance for requirements analysis. The task aims to find the feasible situations which make the goals diverge within the domain, called boundary conditions (BCs). However, the existing approaches for goal conflict identification fail to find sufficient BCs and general BCs which cover more combinations of circumstances. From the BCs found by these existing approaches, we have observed an interesting phenomenon that there are some pairs of BCs are similar in formula structure, which occurs frequently in the experimental cases. In other words, once a BC is found, a new BC may be discovered quickly by slightly changing the former. It inspires us to develop a local search algorithm named LOGION to find BCs, in which the structural similarity is captured by the neighborhood relation of formulae. Based on structural similarity, LOGION can find a lot of BCs in a short time. Moreover, due to the large number of BCs identified, it potentially selects more general BCs from them. By taking experiments on a set of cases, we show that LOG I ON effectively exploits the structural similarity of BCs. We also compare our algorithm against the two state-of-the-art approaches. The experimental results show that LOGION produces one order of magnitude more BCs than the state-of-the-art approaches and confirm that LOGION finds out more general BCs thanks to a large number of BCs.

The Sentential Decision Diagram (SDD) is a compact and canonical representation of Boolean functions that generalizes the Ordered Binary Decision Diagrams (OBDDs). A variant of SDDs, namely Zero-suppressed Sentential Decision Diagrams (ZSDDs), was proposed recently by using different trimming rules. SDDs are suitable for functions where adjacent input assignments have the same outcome, while ZSDDs are more compact for spare functions. In this paper, we introduce a novel canonical SDD variant, called the Tagged Sentential Decision Diagrams (TSDDs). The key insight of TSDDs is to combine both trimming rules of SDDs and ZSDDs. With both characteristics of SDDs and ZSDDs, the TSDD representation is at least as small as the SDD or ZSDD representation for any Boolean functions. This is also shown in our experimental evaluation.

In recent years, multi-agent epistemic planning has received attention from both dynamic logic and planning communities. Existing implementations of multi-agent epistemic planning are based on compilation into classical planning and suffer from various limitations, such as generating only linear plans, restriction to public actions, and incapability to handle disjunctive beliefs. In this paper, we propose a general representation language for multi-agent epistemic planning where the initial KB and the goal, the preconditions and effects of actions can be arbitrary multi-agent epistemic formulas, and the solution is an action tree branching on sensing results.To support efficient reasoning in the multi-agent KD45 logic, we make use of a normal form called alternative cover disjunctive formula (ACDF). We propose basic revision and update algorithms for ACDF formulas. We also handle static propositional common knowledge, which we call constraints. Based on our reasoning, revision and update algorithms, adapting the PrAO algorithm for contingent planning from the literature, we implemented a multi-agent epistemic planner called MAEP. Our experimental results show the viability of our approach.