Explanation generation frameworks aim to make AI systems’ decisions transparent and understandable to human users. However, generating explanations in uncertain environments characterized by incomplete information and probabilistic models remains a significant challenge. In this paper, we propose a novel framework for generating probabilistic monolithic explanations and model reconciling explanations. Monolithic explanations provide self-contained reasons for an explanandum without considering the agent receiving the explanation, while model reconciling explanations account for the knowledge of the agent receiving the explanation. For monolithic explanations, our approach integrates uncertainty by utilizing probabilistic logic to increase the probability of the explanandum. For model reconciling explanations, we propose a framework that extends the logic-based variant of the model reconciliation problem to account for probabilistic human models, where the goal is to find explanations that increase the probability of the explanandum while minimizing conflicts between the explanation and the probabilistic human model. We introduce explanatory gain and explanatory power as quantitative metrics to assess the quality of these explanations. Further, we present algorithms that exploit the duality between minimal correction sets and minimal unsatisfiable sets to efficiently compute both types of explanations in probabilistic contexts. Extensive experimental evaluations on various benchmarks demonstrate the effectiveness and scalability of our approach in generating explanations under uncertainty.

We investigate the belief revision problem in epistemic planning, i.e., what will be the beliefs of all agents in a multi-agent system after an agent gains the belief in some state property. Based on the standard representation in epistemic planning of agents' beliefs via a single multi-agent Kripke model, we generalize the classical AGM belief revision postulates to the multi-agent setting, with the aim to provide a formal framework for evaluating dynamic epistemic reasoning frameworks in which the beliefs of all agents as the result of actions are computed. As an example of a simple operator that satisfies all of the generalized AGM postulates, we present generalized full-meet multi-agent belief revision. We moreover define a generalization of the standard postulates for iterated revision, present a more sophisticated, event model based revision operator, and discuss the potential issues in defining an epistemic operator on Kripke models that can satisfy all of the generalized postulates for iterated multi-agent belief revision.

Acquiring correct action theories from informal specifications remains a central challenge in KR. Large Language Models can generate plausible domain models from natural language, but the resulting theories frequently contain missing preconditions, incorrect effects, or superfluous actions. Existing refinement approaches either require human experts to correct these errors or assume that the input specification is itself correct. We present a fully automated framework that iteratively refines LLM-generated action theories using formal explanations grounded in SAT-based verification. Each candidate theory is encoded as a bounded SAT problem and tested against solvable tasks, which must admit a valid plan, and unsolvable tasks, which must be correctly rejected. When a test fails, we extract a formal explanation that pinpoints the specific theory constraints responsible for the failure, and feed this explanation back to the LLM to guide its next revision. Our initial evaluation across six planning domains shows that our framework can converge to correct theories.

Md Nayem Uddin, Amir Saeidi, Divij Handa, Agastya Seth, Tran Cao Son, Eduardo Blanco, Steven Corman, Chitta Baral. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2025.

Reasoning about Actions and Change (RAC) has historically played a pivotal role in solving foundational AI problems, such as the frame problem. It has driven advancements in AI fields, such as non-monotonic and commonsense reasoning. RAC remains crucial for AI systems that operate in dynamic environments, engage in interactive scenarios, or rely on commonsense reasoning. Despite substantial advances made by Large Language Models (LLMs) in various AI domains, their performance in RAC remains underexplored. To address this gap, we introduce a new diagnostic benchmark, $\textbf{ActionReasoningBench}$, which encompasses 8 domains and includes questions for up to 19 action sequences. This benchmark rigorously evaluates LLMs across six key RAC dimensions: $\textit{Fluent Tracking}$, $\textit{State Tracking}$, $\textit{Action Executability}$, $\textit{Effects of Actions}$, $\textit{Numerical RAC}$, and $\textit{Composite Questions}$. LLMs demonstrate average accuracy rates of 73.55%, 65.63%, 58.73%, and 62.38% on the former four dimensions, which are frequently discussed in RAC literature. However, the performance on the latter two dimensions, which introduce complex and novel reasoning questions, the average performance of LLMs is lowered to 33.16% and 51.19%, respectively, reflecting a 17.9% performance decline. We also introduce new ramification constraints to capture the indirect effects of actions, providing deeper insights into RAC challenges. Our evaluation of state-of-the-art LLMs, including both open-source and commercial models, reveals challenges across all RAC dimensions, particularly in handling ramifications, with GPT-4o failing to solve any question and o1-preview achieving a score of only 18.4%.

Explanation generation frameworks aim to make AI systems’ decisions transparent and understandable to human users. However, generating explanations in uncertain environments characterized by incomplete information and probabilistic models remains a significant challenge. In this paper, we propose a novel framework for generating probabilistic monolithic explanations and model reconciling explanations. Monolithic explanations provide self-contained reasons for an explanandum without considering the agent receiving the explanation, while model reconciling explanations account for the knowledge of the agent receiving the explanation. For monolithic explanations, our approach integrates uncertainty by utilizing probabilistic logic to increase the probability of the explanandum. For model reconciling explanations, we propose a framework that extends the logic-based variant of the model reconciliation problem to account for probabilistic human models, where the goal is to find explanations that increase the probability of the explanandum while minimizing conflicts between the explanation and the probabilistic human model. We introduce explanatory gain and explanatory power as quantitative metrics to assess the quality of these explanations. Further, we present algorithms that exploit the duality between minimal correction sets and minimal unsatisfiable sets to efficiently compute both types of explanations in probabilistic contexts. Extensive experimental evaluations on various benchmarks demonstrate the effectiveness and scalability of our approach in generating explanations under uncertainty.

This paper presents a novel semantics for the mA* epistemic action language that takes into consideration dynamic per-agent observability of events. Different from the original mA* semantics, the observability of events is defined locally at the level of possible worlds, giving a new method for compiling event models. Locally defined observability represents agents' uncertainty and false-beliefs about each others' ability to observe events. This allows for modeling second-order false-belief tasks where one agent does not know the truth about another agent's observations and resultant beliefs. The paper presents detailed constructions of event models for ontic, sensing, and truthful announcement action occurrences and proves various properties relating to agents' beliefs after the execution of an action. It also shows that the proposed approach can model second order false-belief tasks and satisfies the robustness and faithfulness criteria discussed by Bolander (2018, https://doi.org/10. 1007/978-3-319-62864-6_8).

We present a novel framework designed to extend model reconciliation approaches, commonly used in human-aware planning, for enhanced human-AI interaction. By adopting a structured argumentation-based dialogue paradigm, our framework enables dialectical reconciliation to address knowledge discrepancies between an explainer (AI agent) and an explainee (human user), where the goal is for the explainee to understand the explainer's decision. We formally describe the operational semantics of our proposed framework, providing theoretical guarantees. We then evaluate the framework's efficacy ``in the wild'' via computational and human-subject experiments. Our findings suggest that our framework offers a promising direction for fostering effective human-AI interactions in domains where explainability is important.

Abstract Explainable artificial intelligence (XAI) aims at addressing complex problems by coupling solutions with reasons that justify the provided answer. In the context of Answer Set Programming (ASP) the user may be interested in linking the presence or absence of an atom in an answer set to the logic rules involved in the inference of the atom. Such explanations can be given in terms of directed acyclic graphs (DAGs). This article reports on the advancements in the development of the XAI system xASP by revising the main foundational notions and by introducing new ASP encodings to compute minimal assumption sets, explanation sequences, and explanation DAGs. DAGs are shown to the user in an interactive form via the xASP navigator application, also introduced in this work.

In multi-agent systems, multi-agent planning and diagnosis are two key subfields – multi-agent planning approaches identify plans for the agents to execute in order to reach their goals, and multi-agent diagnosis approaches identify root causes for faults when they occur, typically by using information from the multi-agent planning model as well as the resulting multi-agent plan. However, when a plan fails during execution, the cause can often be related to some commonsense information that is neither explicitly encoded in the planning nor diagnosis problems. As such existing diagnosis approaches fail to accurately identify the root causes in such situations.

Abstract A plethora of approaches have been proposed for joint entity-relation (ER) extraction. Most of these methods largely depend on a large amount of manually annotated training data. However, manual data annotation is time-consuming, labor-intensive, and error-prone. Human beings learn using both data (through induction) and knowledge (through deduction). Answer Set Programming (ASP) has been a widely utilized approach for knowledge representation and reasoning that is elaboration tolerant and adept at reasoning with incomplete information. This paper proposes a new approach, ASP-enhanced Entity-Relation extraction (ASPER), to jointly recognize entities and relations by learning from both data and domain knowledge. In particular, ASPER takes advantage of the factual knowledge (represented as facts in ASP) and derived knowledge (represented as rules in ASP) in the learning process of neural network models. We have conducted experiments on two real datasets and compare our method with three baselines. The results show that our ASPER model consistently outperforms the baselines.

Abstract This paper introduces a formal definition of a Cyber-Physical System (CPS) in the spirit of the CPS Framework proposed by the National Institute of Standards and Technology (NIST). It shows that using this definition, various problems related to concerns in a CPS can be precisely formalized and implemented using Answer Set Programming (ASP). These include problems related to the dependency or conflicts between concerns, how to mitigate an issue, and what the most suitable mitigation strategy for a given issue would be. It then shows how ASP can be used to develop an implementation that addresses the aforementioned problems. The paper concludes with a discussion of the potentials of the proposed methodologies.

Abstract Answer Set Planningrefers to the use ofAnswer Set Programming (ASP)to computeplans, that is, solutions to planning problems, that transform a given state of the world to another state. The development of efficient and scalable answer set solvers has provided a significant boost to the development of ASP-based planning systems. This paper surveys the progress made during the last two and a half decades in the area of answer set planning, from its foundations to its use in challenging planning domains. The survey explores the advantages and disadvantages of answer set planning. It also discusses typical applications of answer set planning and presents a set of challenges for future research.

In human-aware planning systems, a planning agent might need to explain its plan to a human user when that plan appears to be non-feasible or sub-optimal. A popular approach, called model reconciliation, has been proposed as a way to bring the model of the human user closer to the agent’s model. To do so, the agent provides an explanation that can be used to update the model of human such that the agent’s plan is feasible or optimal to the human user. Existing approaches to solve this problem have been based on automated planning methods and have been limited to classical planning problems only. In this paper, we approach the model reconciliation problem from a different perspective, that of knowledge representation and reasoning, and demonstrate that our approach can be applied not only to classical planning problems but also hybrid systems planning problems with durative actions and events/processes. In particular, we propose a logic-based framework for explanation generation, where given a knowledge base KBa (of an agent) and a knowledge base KBh (of a human user), each encoding their knowledge of a planning problem, and that KBa entails a query q (e.g., that a proposed plan of the agent is valid), the goal is to identify an explanation ε ⊆ KBa such that when it is used to update KBh, then the updated KBh also entails q. More specifically, we make the following contributions in this paper: (1) We formally define the notion of logic-based explanations in the context of model reconciliation problems; (2) We introduce a number of cost functions that can be used to reflect preferences between explanations; (3) We present algorithms to compute explanations for both classical planning and hybrid systems planning problems; and (4) We empirically evaluate their performance on such problems. Our empirical results demonstrate that, on classical planning problems, our approach is faster than the state of the art when the explanations are long or when the size of the knowledge base is small (e.g., the plans to be explained are short). They also demonstrate that our approach is efficient for hybrid systems planning problems. Finally, we evaluate the real-world efficacy of explanations generated by our algorithms through a controlled human user study, where we develop a proof-of-concept visualization system and use it as a medium for explanation communication.

Research in explainable planning is becoming increasingly important as human-AI collaborations become more pervasive. An explanation is needed when the planning system’s solution does not match the human’s expectation. In this paper, we introduce the explainability problem in clingo-dl programs (XASP-D) because clingo-dl can effectively work with numerical scheduling, a problem similar to the explainable planning.

Abstract We present a general approach to planning with incomplete information in Answer Set Programming (ASP). More precisely, we consider the problems of conformant and conditional planning with sensing actions and assumptions. We represent planning problems using a simple formalism where logic programs describe the transition function between states, the initial states and the goal states. For solving planning problems, we use Quantified Answer Set Programming (QASP), an extension of ASP with existential and universal quantifiers over atoms that is analogous to Quantified Boolean Formulas (QBFs). We define the language of quantified logic programs and use it to represent the solutions different variants of conformant and conditional planning. On the practical side, we present a translation-based QASP solver that converts quantified logic programs into QBFs and then executes a QBF solver, and we evaluate experimentally the approach on conformant and conditional planning benchmarks.

The paper proposes a framework for capturing how an agent’s beliefs evolve over time in response to observations and for answering the question of whether statements made by a third party can be believed. The basic components of the framework are a formalism for reasoning about actions, changes, and observations and a formalism for default reasoning. The paper describes a concrete implementation that leverages answer set programming for determining the evolution of an agent's ``belief state'', based on observations, knowledge about the effects of actions, and a theory about how these influence an agent's beliefs. The beliefs are then used to assess whether statements made by a third party can be accepted as truthful. The paper investigates an application of the proposed framework in the detection of man-in-the-middle attacks targeting computers and cyber-physical systems. Finally, we briefly discuss related work and possible extensions.

In human-aware planning problems, the planning agent may need to explain its plan to a human user, especially when the plan appears infeasible or suboptimal for the user. A popular approach to do so is called model reconciliation, where the planning agent tries to reconcile the differences between its model and the model of the user such that its plan is also feasible and optimal to the user. This problem can be viewed as an optimization problem, where the goal is to find a subset-minimal explanation that one can use to modify the model of the user such that the plan of the agent is also feasible and optimal to the user. This paper presents an algorithm for solving such problems using answer set programming.

The paper introduces the notion of an epistemic argumentation framework (EAF) as a means to integrate the beliefs of a reasoner with argumentation. Intuitively, an EAF encodes the beliefs of an agent who reasons about arguments. Formally, an EAF is a pair of an argumentation framework and an epistemic constraint. The semantics of the EAF is defined by the notion of an ω-epistemic labelling set, where ω is complete, stable, grounded, or preferred, which is a set of ω-labellings that collectively satisfies the epistemic constraint of the EAF. The paper shows how EAF can represent different views of reasoners on the same argumentation framework. It also includes representing preferences in EAF and multi-agent argumentation. Finally, the paper discusses complexity issues and computation using epistemic logic programming.

Abstract The paper describes an ongoing effort in developing a declarative system for supporting operators in the Nuclear Power Plant (NPP) control room. The focus is on two modules: diagnosis and explanation of events that happened in NPPs. We describe an Answer Set Programming (ASP) representation of an NPP, which consists of declarations of state variables, components, their connections, and rules encoding the plant behavior. We then show how the ASP program can be used to explain the series of events that occurred in the Three Mile Island, Unit 2 (TMI-2) NPP accident, the most severe accident in the USA nuclear power plant operating history. We also describe an explanation module aimed at addressing answers to questions such as “why an event occurs?” or “what should be done?” given the collected data.

We study defeasible knowledge bases with conditional preferences (DKB). A DKB consists of a set of undisputed facts and a rule-based system that contains different types of rules: strict, defeasible, and preference. A major challenge in defining the semantics of DKB lies in determining how conditional preferences interact with the attack relations represented by rebuts and undercuts, between arguments. We introduce the notions of preference attack relations as sets of attacks between preference arguments and the rebuts or undercuts among arguments as well as of preference attack relation assignments which map knowledge bases to preference attack relations. We present five rational properties (referred to as regular properties), the inconsistency-resolving, effective rebuts, context-independence, attack monotonicity and link-orientation properties generalizing the properties of the same names for the case of unconditional preferences. Preference attack relation assignment are defined as regular if they satisfy all regular properties. We show that the set of regular assignments forms a complete lower semilattice whose least element is referred to as the canonical preference attack relation assignment. Canonical attack relation assignment represents the semantics of preferences in defeasible knowledge bases as intuitively, it could be viewed as being uniquely identified by the regular properties together with the principle of minimal removal of undesired attacks. We also present the normal preference attack relation assignment as an approximation of the canonical attack relation assignment.

Multi-Agent Path Finding (MAPF) problems are traditionally solved in a centralized manner. There are works focusing on completeness, optimality, performance, or a tradeoff between them. However, there are only a few works based on spatial distribution. In this paper, we introduce ros-dmapf, a distributed MAPF solver. It consists of multiple MAPF sub-solvers, which---besides solving their assigned sub-problems---interact with each other to solve a given MAPF problem. In the current implementation, the sub-solvers are answer set planning systems for multiple agents, and are created based on spatial distribution of the problem. Interactions between components of ros-dmapf are facilitated by the Robot Operating System (ROS). The highlights of ros-dmapf are its scalability and a high degree of parallelism. We empirically evaluate ros-dmapf using the move-only domain of the asprilo system and results suggest that ros-dmapf scales up well. For instance, ros-dmapf gives a solution of length around 600 for a MAPF problem with 2000 robots in randomly generated 100×100 obstacle-free maps---a problem beyond the capability of a single sub-solver---within 7 minutes on a consumer laptop. We also evaluate ros-dmapf against some other MAPF solvers and results show that the system performs well. We also discuss possible improvements for future work.

In Multi-Agent Path Finding (MAPF), a team of agents needs to find collision-free paths from their starting locations to their respective targets. Combined Target Assignment and Path Finding (TAPF) extends MAPF by including the problem of assigning targets to agents as a precursor to the MAPF problem. A limitation of both models is their assumption that the number of agents and targets are equal, which is invalid in some applications. We address this limitation by generalizing TAPF to allow for (1) unequal number of agents and tasks; (2) tasks to have deadlines by which they must be completed; (3) ordering of groups of tasks to be completed; and (4) tasks that are composed of a sequence of checkpoints that must be visited in a specific order. Further, we model the problem using answer set programming (ASP) to show that customizing the desired variant of the problem is simple -- one only needs to choose the appropriate combination of ASP rules to enforce it. We also demonstrate experimentally that if problem specific information can be incorporated into the ASP encoding then ASP based methods can be efficient and can scale up to solve practical applications.

This paper presents an extension of the Multi-Context Systems (MCS) framework to allow the encoding of preferences at the level of the contexts. The work is motivated by the observation that a naive use of preference logics at a context level in an MCS can lead to undesirable outcomes, such as inco nsistency of the MCS. To address this issue, the paper introduces the notion of ranked logics, suitable for use with multiple sources of preferences, and employs them in the definition of weakly and strongly-preferred equilibria in a Multi-Context Systems with Preferences (MCSP) framework. The usefulness of MCSP is demonstrated in two applications: modeling distributed configuration problems and finding explanations for distributed abductive diagnosis problems.

The fourteenth International Workshop on Computational Logic in Multi-Agent Systems (CLIMA XIV) was held in Coruña, Spain, 16–18 September 2013. The final programme included 23 papers and 30 participants attended the workshop. This special issue contains six papers from the workshop that discuss a variety of issues central to the use of logic in reasoning about multi-agent systems. The first paper in the collection, ‘The Equivalence Zoo for Dung-style Semantics’ by Baumann and Brewka, presents an extensive study of seven equivalence notions (standard, normal, strong, weak, and local expansion and minimal change equivalence) under major semantics of Dung's argumentation framework (stable, preferred, admissible and complete semantics). It shows that minimal change equivalence is a reasonable notion of equivalence between argumentation frameworks. The paper also investigates the aforementioned relationship with respect to the two restricted classes of argumentation frameworks that have the same arguments and/or are self-loop-free. The second paper, ‘Two-stage Agent Program Verification’ by Dennis, Fisher and Webster, proposes a novel method for verification of agent programs that are written in a Belief–Desire–Intention (BDI) programming language using program model-checkers. The paper extends the Agent Java Pathfinder (AJPF) agent program model-checker to generate models that could be used by other model-checkers. The key idea behind the approach lies in that generated models could be used for several purposes (e.g. proving different properties of a program). The paper demonstrates the new technique by describing the export of the AJPF program models to both the SPIN and P rism model-checkers.

Abstract We introduce theasprilo1framework to facilitate experimental studies of approaches addressing complex dynamic applications. For this purpose, we have chosen the domain of robotic intra-logistics. This domain is not only highly relevant in the context of today's fourth industrial revolution but it moreover combines a multitude of challenging issues within a single uniform framework. This includes multi-agent planning, reasoning about action, change, resources, strategies, etc. In return,aspriloallows users to study alternative solutions as regards effectiveness and scalability. Althoughasprilorelies on Answer Set Programming and Python, it is readily usable by any system complying with its fact-oriented interface format. This makes it attractive for benchmarking and teaching well beyond logic programming. More precisely,aspriloconsists of a versatile benchmark generator, solution checker and visualizer as well as a bunch of reference encodings featuring various ASP techniques. Importantly, the visualizer's animation capabilities are indispensable for complex scenarios like intra-logistics in order to inspect valid as well as invalid solution candidates. Also, it allows for graphically editing benchmark layouts that can be used as a basis for generating benchmark suites.

Abstract Evolutionary Biologists have long struggled with the challenge of developing analysis workflows in a flexible manner, thus facilitating the reuse of phylogenetic knowledge. An evolutionary biology workflow can be viewed as a plan which composes web services that can retrieve, manipulate, and produce phylogenetic trees. The Phylotastic project was launched two years ago as a collaboration between evolutionary biologists and computer scientists, with the goal of developing an open architecture to facilitate the creation of such analysis workflows. While composition of web services is a problem that has been extensively explored in the literature, including within the logic programming domain, the incarnation of the problem in Phylotastic provides a number of additional challenges. Along with the need to integrate preferences and formal ontologies in the description of the desired workflow, evolutionary biologists tend to construct workflows in an incremental manner, by successively refining the workflow, by indicating desired changes (e.g., exclusion of certain services, modifications of the desired output). This leads to the need of successive iterations of incremental replanning, to develop a new workflow that integrates the requested changes while minimizing the changes to the original workflow. This paper illustrates how Phylotastic has addressed the challenges of creating and refining phylogenetic analysis workflows using logic programming technology and how such solutions have been used within the general framework of the Phylotastic project.

In Multi-Agent Path Finding (MAPF), a team of agents needs to find collision-free paths from their starting locations to their respective targets. Combined Target Assignment and Path Finding (TAPF) extends MAPF by including the problem of assigning targets to agents as a precursor to the MAPF problem. A limitation of both models is their assumption that the number of agents and targets are equal, which is invalid in some applications such as autonomous warehouse systems. We address this limitation by generalizing TAPF to allow for (1)~unequal number of agents and tasks; (2)~tasks to have deadlines by which they must be completed; (3)~ordering of groups of tasks to be completed; and (4)~tasks that are composed of a sequence of checkpoints that must be visited in a specific order. Further, we model the problem using answer set programming (ASP) to show that customizing the desired variant of the problem is simple one only needs to choose the appropriate combination of ASP rules to enforce it. We also demonstrate experimentally that if problem specific information can be incorporated into the ASP encoding then ASP based method can be efficient and can scale up to solve practical applications.

This paper presents a novel algorithm for computing world views of different semantics of epistemic logic programs (ELP) and two of its realization, called Ep-asp (for an older semantics) and Ep-asp^{se} (for the newest semantics), whose implementation builds on the theoretical advancement in the study of ELPs and takes advantage of the multi-shot computation paradigm of the answer set solver Clingo. The new algorithm differs from the majority of earlier algorithms in its strategy. Specifically, it computes one world view at a time and utilizes properties of world views to reduce its search space. It starts by computing an answer set and then determines whether or not a world view containing this answer set exists. In addition, it allows for the computation to focus on world views satisfying certain properties. The paper includes an experimental analysis of the performance of the two solvers comparing against a recently developed solver. It also contains an analysis of their performance in goal directed computing against a logic programming based conformant planning system, dlv-k. It concludes with some final remarks and discussion on the future work.

This paper proposes a generic approach to planning in the presence of incomplete information. The approach builds on an abstract notion of a belief state representation, along with an associated set of basic operations. These operations facilitate the development of a sound and complete transition function, for reasoning about effects of actions in the presence of incomplete information, and a set of abstract algorithms for planning. The paper demonstrates how the abstract definitions and algorithms can be instantiated in three concrete representations—minimal-DNF, minimal-CNF, and prime implicates—resulting in three highly competitive conformant planners: DNF, CNF, and PIP. The paper relates the notion of a representation to that of ordered binary decision diagrams, a well-known belief state representation employed by many conformant planners, and several target compilation languages that have been presented in the literature.The paper also includes an experimental evaluation of the planners DNF, CNF, and PIP and proposes a new set of conformant planning benchmarks that are challenging for state-of-the-art conformant planners.

Abstract This paper explores the use ofAnswer Set Programming (ASP)in solvingDistributed Constraint Optimization Problems (DCOPs). The paper provides the following novel contributions: (1) it shows how one can formulate DCOPs as logic programs; (2) it introduces ASP-DPOP, the first DCOP algorithm that is based on logic programming; (3) it experimentally shows that ASP-DPOP can be up to two orders of magnitude faster than DPOP (its imperative programming counterpart) as well as solve some problems that DPOP fails to solve, due to memory limitations; and (4) it demonstrates the applicability of ASP in a wide array of multi-agent problems currently modeled as DCOPs.

This special issue of Theory and Practice of Logic Programming (TPLP) contains the regular papers accepted for presentation at the 33rd International Conference on Logic Programming (ICLP 2017), held in Melbourne, Australia from the 28th of August to the 1st of September, 2017. ICLP 2017 was colocated with the 23rd International Conference on Principles and Practice of Constraint Programming (CP 2017) and the 20th International Conference on Theory and Applications of Satisfiability Testing (SAT 2017). Since the first conference held in Marseille in 1982, ICLP has been the premier international event for presenting research in logic programming.

Goal Recognition Design involves identifying the best ways to modify an underlying environment that agents operate in, typically by making asubset of feasible actions infeasible, so that agents are forced to reveal their goals as early as possible. Thus far, existing work has focused exclusively on imperative classical planning. In this paper, we address the same problem with a different paradigm, namely, declarative approaches based on Answer Set Programming (ASP). Our experimental results show that one of our ASP encodings is more scalable and is significantly faster by up to three orders of magnitude than thecurrent state of the art.

This paper explores the use of answer set programming (ASP) in solving distributed constraint optimization problems (DCOPs). It makes the following contributions: (i)~It shows how one can formulate DCOPs as logic programs; (ii)~It introduces ASP-DPOP, the first DCOP algorithm that is based on logic programming; (iii)~It experimentally shows that ASP-DPOP can be up to two orders of magnitude faster than DPOP (its imperative-programming counterpart) as well as solve some problems that DPOP fails to solve due to memory limitations; and (iv)~It demonstrates the applicability of ASP in the wide array of multi-agent problems currently modeled as DCOPs.

The paper proposes a condition for preserving the KD45 property of a Kripke model when a sequence of update models is applied to it. The paper defines the notions of a primitive update model and a semi-reflexive KD45 (or sr-KD45) Kripke model. It proves that updating a sr-KD45 Kripke model using a primitive update model results in a sr-KD45 Kripke model, i.e., a primitive update model preserves the properties of a sr-KD45 Kripke model. It shows that several update models for modeling well-known actions found in the literature are primitive. This result provides guarantees that can be useful in presence of multiple applications of actions in multi-agent system (e.g., multi-agent planning).

This paper explores the use of Binary Decision Diagrams (BDDs) in Conformant Planning. A conformant planner, called BPA, based on the BDD representation for belief sets is developed. Heuristics that fit with the BDD representation are presented and analyzed experimentally. The paper confirms the strong potential of BDDs to enhance performance of heuristic search based conformant planners.

While MDPs are powerful tools for modeling sequential decision making problems under uncertainty, they are sensitive to the accuracy of their parameters. MDPs with uncertainty in their parameters are called Uncertain MDPs. In this paper, we introduce a general framework that allows off-the-shelf MDP algorithms to solve Uncertain MDPs by planning based on currently available information and replan if and when the problem changes. We demonstrate the generality of this approach by showing that it can use the VI, TVI, ILAO*, LRTDP, and UCT algorithms to solve Uncertain MDPs. We experimentally show that our approach is typically faster than replanning from scratch and we also provide a way to estimate the amount of speedup based on the amount of information being reused.

The article introduces a logical framework for negotiation among dishonest agents. The framework relies on the use of abductive logic programming as a knowledge representation language for agents to deal with incomplete information and preferences. The article shows how intentionally false or inaccurate information of agents can be encoded in the agents' knowledge bases. Such disinformation can be effectively used in the process of negotiation to have desired outcomes by agents. The negotiation processes are formulated under the answer set semantics of abductive logic programming, and they enable the exploration of various strategies that agents can employ in their negotiation. A preliminary implementation has been developed using the ASP-Prolog platform.

This article describes the planner C p A( H ), the recipient of the Best Nonobservable Nondeterministic Planner Award in the “Uncertainty Track” of the 6 th International Planning Competition (IPC), 2008. The article presents the various techniques that help C p A( H ) to achieve the level of performance and scalability exhibited in the competition. The article also presents experimental results comparing C p A( H ) with state-of-the-art conformant planners.

Information extraction systems are traditionally implemented as a pipeline of special-purpose processing modules targeting the extraction of a particular kind of information. A major drawback of such an approach is that whenever a new extraction goal emerges or a module is improved, extraction has to be reapplied from scratch to the entire text corpus even though only a small part of the corpus might be affected. In this paper, we describe a novel approach for information extraction in which extraction needs are expressed in the form of database queries, which are evaluated and optimized by database systems. Using database queries for information extraction enables generic extraction and minimizes reprocessing of data by performing incremental extraction to identify which part of the data is affected by the change of components or goals. Furthermore, our approach provides automated query generation components so that casual users do not have to learn the query language in order to perform extraction. To demonstrate the feasibility of our incremental extraction approach, we performed experiments to highlight two important aspects of an information extraction system: efficiency and quality of extraction results. Our experiments show that in the event of deployment of a new module, our incremental extraction approach reduces the processing time by 89.64 percent as compared to a traditional pipeline approach. By applying our methods to a corpus of 17 million biomedical abstracts, our experiments show that the query performance is efficient for real-time applications. Our experiments also revealed that our approach achieves high quality extraction results.

The paper introduces a novel technique for improving the performance and scalability of best-first progression-based conformant planners. The technique is inspired by different well-known techniques from classical planning, such as landmark and stratification. Its most salient feature is that it is relatively cheap to implement yet quite effective when applicable. The effectiveness of the proposed technique is demonstrated by the development of new conformant planners by integrating the technique in various state-of-the-art conformant planners and an extensive experimental evaluation of the new planners using benchmarks collected from various sources. The result shows that the technique can be applied in several benchmarks and helps improve both performance and scalability of conformant planners.

This paper compares in depth the effectiveness of two conjunctive belief state representations in contingent planning: prime implicates and minimal CNF, a compact form of CNF formulae, which were initially proposed in conformant planning research (To et al. 2010a; 2010b). Similar to the development of the contingent planner CNFct for minimal CNF (To et al. 2011b), the present paper extends the progression function for the prime implicate representation in (To et al. 2010b) for computing successor belief states in the presence of incomplete information to handle non-deterministic and sensing actions required in contingent planning. The idea was instantiated in a new contingent planner, called PIct, using the same AND/OR search algorithm and heuristic function as those for CNFct. The experiments show that, like CNFct, PIct performs very well in a wide range of benchmarks. The study investigates the advantages and disadvantages of the two planners and identifies the properties of each representation method that affect the performance.

This work proposes new approaches to contingent planning using alternative belief state representations extended from those in conformant planning and a new AND/OR forward search algorithm, called PrAO, for contingent solutions. Each representation was implemented in a new contingent planner. The important role of belief state representation has been confirmed by the fact that our planners all outperform other stateof- the-art planners on most benchmarks and the comparison of their performances varies across all the benchmarks even using the same search algorithm PrAO and same unsophisticated heuristic scheme. The work identifies the properties of each representation method that affect the performance.

The paper introduces a logical framework for negotiation among dishonest agents. The framework relies on the use of abductive logic programming as a knowledge representation language for agents to deal with incomplete information and preferences. The paper shows how intentionally false or inaccurate information of agents could be encoded in the agents' knowledge bases. Such disinformation can be effectively used in the process of negotiation to have desired outcomes by agents. The negotiation processes are formulated under the answer set semantics of abductive logic programming and enable the exploration of various strategies that agents can employ in their negotiation.

This paper investigates the effectiveness of two state representations, CNF and DNF, in contingent planning. To this end, we developed a new contingent planner, called CNFct, using the AND/OR forward search algorithm PrAO [To et al., 2011] and an extension of the CNF representation of [To et al., 2010] for conformant planning to handle nondeterministic and sensing actions for contingent planning. The study uses CNFct and DNFct [To et al., 2011] and proposes a new heuristic function for both planners. The experiments demonstrate that both CNFct and DNFct offer very competitive performance in a large range of benchmarks but neither of the two representations is a clear winner over the other. The paper identifies properties of the representation schemes that can affect their performance on different problems.

BACKGROUND: The Comparative Data Analysis Ontology (CDAO) is an ontology developed, as part of the EvoInfo and EvoIO groups supported by the National Evolutionary Synthesis Center, to provide semantic descriptions of data and transformations commonly found in the domain of phylogenetic analysis. The core concepts of the ontology enable the description of phylogenetic trees and associated character data matrices. RESULTS: Using CDAO as the semantic back-end, we developed a triple-store, named CDAO-Store. CDAO-Store is a RDF-based store of phylogenetic data, including a complete import of TreeBASE. CDAO-Store provides a programmatic interface, in the form of web services, and a web-based front-end, to perform both user-defined as well as domain-specific queries; domain-specific queries include search for nearest common ancestors, minimum spanning clades, filter multiple trees in the store by size, author, taxa, tree identifier, algorithm or method. In addition, CDAO-Store provides a visualization front-end, called CDAO-Explorer, which can be used to view both character data matrices and trees extracted from the CDAO-Store. CDAO-Store provides import capabilities, enabling the addition of new data to the triple-store; files in PHYLIP, MEGA, nexml, and NEXUS formats can be imported and their CDAO representations added to the triple-store. CONCLUSIONS: CDAO-Store is made up of a versatile and integrated set of tools to support phylogenetic analysis. To the best of our knowledge, CDAO-Store is the first semantically-aware repository of phylogenetic data with domain-specific querying capabilities. The portal to CDAO-Store is available at http://www.cs.nmsu.edu/~cdaostore.

The paper presents an investigation of the use of two alternative forms of CNF formulae—prime implicates and minimal CNF—to compactly represent belief states in the context of conformant planning. For each representation, we define a transition function for computing the successor belief state resulting from the execution of an action in a belief state; results concerning soundness and completeness are provided. The paper describes a system (PIP) which dynamically selects either of these two forms to represent belief states, and an experimental evaluation of PIP against state-of-the-art conformant planners. The results show that PIP has the potential of scaling up better than other planners in problems rich in disjunctive information about the initial state.

Information extraction systems are traditionally implemented as a pipeline of special-purpose processing modules. A major drawback of such an approach is that whenever a new extraction goal emerges or a module is improved, extraction has to be re-applied from scratch to the entire text corpus even though only a small part of the corpus might be affected. In this demonstration proposal, we describe a novel paradigm for information extraction: we store the parse trees output by text processing in a database, and then express extraction needs using queries, which can be evaluated and optimized by databases. Compared with the existing approaches, database queries for information extraction enable generic extraction and minimize reprocessing. However, such an approach also poses a lot of technical challenges, such as language design, optimization and automatic query generation. We will present the opportunities and challenges that we met when building GenerIE, a system that implements this paradigm.

Abstract The logics of knowledge are modal logics that have been shown to be effective in representing and reasoning about knowledge in multi-agent domains. Relatively few computational frameworks for dealing with computation of models and useful transformations in logics of knowledge (e.g., to support multi-agent planning with knowledge actions and degrees of visibility) have been proposed. This paper explores the use of logic programming (LP) to encode interesting forms of logics of knowledge and compute Kripke models. The LP modeling is expanded with useful operators on Kripke structures, to support multi-agent planning in the presence of both world-altering and knowledge actions. This results in the first ever implementation of a planner for this type of complex multi-agent domains.

Several solutions, based on aural and haptic feedback, have been developed to enable access to complex on-line information for people with visual impairments. Nevertheless, there are several components of widely used software applications that are still beyond the reach of screen readers and Braille displays.

Abstract The goal of this paper is to investigate the application of parallel programming techniques to boost the performance of heuristic search‐based planning systems in various aspects. It shows that an appropriate parallelization of a sequential planning system often brings gain in performance and/or scalability. We start by describing general schemes for parallelizing the construction of a plan. We then discuss the applications of these techniques to two domain‐independent heuristic search‐based planners—a competitive conformant planner (CPA) and a state‐of‐the‐art classical planner (FF). We present experimental results—on both shared memory and distributed memory platforms—which show that the performance improvements and scalability are obtained in both cases. Finally, we discuss the issues that should be taken into consideration when designing a parallel planning system and relate our work to the existing literature. Copyright © 2009 John Wiley & Sons, Ltd.

Abstract The paper introduces the notion of offline justification for answer set programming (ASP). Justifications provide a graph-based explanation of the truth value of an atom with respect to a given answer set. The paper extends also this notion to provide justification of atoms during the computation of an answer set (on-line justification) and presents an integration of online justifications within the computation model of Smodels. Offline and online justifications provide useful tools to enhance understanding of ASP, and they offer a basic data structure to support methodologies and tools for debugging answer set programs. A preliminary implementation has been developed in – .

In this paper, we present two alternative approaches to defining answer sets for logic programs with arbitrary types of abstract constraint atoms (c-atoms). These approaches generalize the fixpoint-based and the level mapping based answer set semantics of normal logic programs to the case of logic programs with arbitrary types of c-atoms. The results are four different answer set definitions which are equivalent when applied to normal logic programs. The standard fixpoint-based semantics of logic programs is generalized in two directions, called answer set by reduct and answer set by complement. These definitions, which differ from each other in the treatment of negation-as-failure (naf) atoms, make use of an immediate consequence operator to perform answer set checking, whose definition relies on the notion of conditional satisfaction of c-atoms w.r.t. a pair of interpretations. The other two definitions, called strongly and weakly well-supported models, are generalizations of the notion of well-supported models of normal logic programs to the case of programs with c-atoms. As for the case of fixpoint-based semantics, the difference between these two definitions is rooted in the treatment of naf atoms. We prove that answer sets by reduct (resp. by complement) are equivalent to weakly (resp. strongly) well-supported models of a program, thus generalizing the theorem on the correspondence between stable models and well-supported models of a normal logic program to the class of programs with c-atoms. We show that the newly defined semantics coincide with previously introduced semantics for logic programs with monotone c-atoms, and they extend the original answer set semantics of normal logic programs. We also study some properties of answer sets of programs with c-atoms, and relate our definitions to several semantics for logic programs with aggregates presented in the literature.

Abstract This technical note describes a monotone and continuous fixpoint operator to compute the answer sets of programs with aggregates. The fixpoint operator relies on the notion ofaggregate solution. Under certain conditions, this operator behaves identically to the three-valued immediate consequence operator ΦaggrPfor aggregate programs, independently proposed in Pelov (2004) and Pelovet al.(2004). This operator allows us to closely tie the computational complexity of the answer set checking and answer sets existence problems to the cost of checking a solution of the aggregates in the program. Finally, we relate the semantics described by the operator to other proposals for logic programming with aggregates.

Abstract We extend the 0-approximation of sensing actions and incomplete information in Son and Baral (2001) to action theories with static causal laws and prove its soundness with respect to the possible world semantics. We also show that the conditional planning problem with respect to this approximation isNP-complete. We then present an answer set programming based conditional planner, called ASCP, that is capable of generating both conformant plans and conditional plans in the presence of sensing actions, incomplete information about the initial state, and static causal laws. We prove the correctness of our implementation and argue that our planner is sound and complete with respect to the proposed approximation. Finally, we present experimental results comparing ASCP to other planners.

We present a state-based regression function for planning domains where an agent does not have complete information and may have sensing actions. We consider binary domains and employ a three-valued characterization of domains with sensing actions to define the regression function. We prove the soundness and completeness of our regression formulation with respect to the definition of progression. More specifically, we show that (i) a plan obtained through regression for a planning problem is indeed a progression solution of that planning problem, and that (ii) for each plan found through progression, using regression one obtains that plan or an equivalent one.

In this article we consider three different kinds of domain-dependent control knowledge (temporal, procedural and HTN-based) that are useful in planning. Our approach is declarative and relies on the language of logic programming with answer set semantics (AnsProlog*). AnsProlog* is designed to plan without control knowledge. We show how temporal, procedural and HTN-based control knowledge can be incorporated into AnsProlog* by the modular addition of a small number of domain-dependent rules, without the need to modify the planner. We formally prove the correctness of our planner, both in the absence and presence of the control knowledge. Finally, we perform some initial experimentation that demonstrates the potential reduction in planning time that can be achieved when procedural domain knowledge is used to solve planning problems with large plan length.

We present a declarative language, ${\cal PP}$ , for the high-level specification of preferences between possible solutions (or trajectories) of a planning problem. This novel language allows users to elegantly express non-trivial, multi-dimensional preferences and priorities over such preferences. The semantics of ${\cal PP}$ allows the identification of most preferred trajectories for a given goal. We also provide an answer set programming implementation of planning problems with ${\cal PP}$ preferences.

This paper shows how action theories, expressed in an extended version of the language , can be naturally encoded using Prioritized Default Theory. We also show how prioritized default theory can be extended to express preferences between rules. This extension provides a natural framework to introduce different types of preferences in action theories—preferences between actions and preferences between final states. In particular, we demonstrate how these preferences can be expressed within extended prioritized default theory. We also discuss how this framework can be implemented in terms of answer set programming.

In this paper, we highlight the main research directions currently pursued by the investigators for the development of new tools to improve Web accessibility for users with visual disabilities. The overall principle is to create intelligent software agents used to assist visually impaired individuals in accessing complex on-line data organizations (e.g. tables, frame structures) in a meaningful way. Accessibility agents make use of knowledge representation structures (automatically or manually derived) to assist users in developing navigation plans; these are employed to locate given pieces of information or to answer user's desired goals.

The search for an appropriate characterization of negation as failure in logic programs in the mid 1980s led to several proposals. Amongst them the stable model semantics – later referred to as answer set semantics, and the well-founded semantics are the most popular and widely referred ones. According to the latest (September 2002) list of most cited source documents in the CiteSeer database (http://citeseer.nj.nec.com) the original stable model semantics paper (Gelfond and Lifschitz, 1988) is ranked 10th with 649 citations and the well-founded semantics paper (Van Gelder et al., 1991) is ranked 70th with 306 citations. Since 1988 – when stable models semantics was proposed – there has been a large body of work centered around logic programs with answer set semantics covering topics such as: systematic program development, systematic program analysis, knowledge representation, declarative problem solving, answer set computing algorithms, complexity and expressiveness, answer set computing systems, relation with other non-monotonic and knowledge representation formalisms, and applications to various tasks.

In this paper we demonstrate how the DSL for Table navigation [16] can be reinterpreted in the context of an action theory [8]. We also show how this generalization provides the ability to carry out more complex tasks such as (i) allowing the user to describe the objective of his/her navigation as a goal and let automatic mechanisms (i.e., a planner) develop (part of) the navigation process; and (ii) allowing the semantic description to predefine not only complete navigation strategies (as in [16]) but also partial skeletons, making the remaining part of the navigation dependent on run-time factors, e.g., user's goals, specific aspects of the table's content, User's run-time decisions.

We present the design and implementation of a loosely-bound SQL extension that allows users to include high-level display specifications with an SQL query, particularly when dealing with multimedia databases. We describe an architecture that allows a relatively simple implementation of dynamic query browsers using the proposed query language on stand-alone applications or World Wide Web pages. We have already implemented most of our proposed extension.