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This paper demonstrates the undecidability of a number of logics with quantification over public announcements: arbitrary public announcement logic (APAL), group announcement logic (GAL), and coalition announcement logic (CAL). In APAL we consider the informative consequences of any announcement, in GAL we consider the informative consequences of a group of agents (this group may be a proper subset of the set of all agents) all of which are simultaneously (and publicly) making known announcements. So this is more restrictive than APAL. Finally, CAL is as GAL except that we now quantify over anything the agents not in that group may announce simultaneously as well. The logic CAL therefore has some features of game logic and of ATL. We show that when there are multiple agents in the language, the satisfiability problem is undecidable for APAL, GAL, and CAL. In the single agent case, the satisfiability problem is decidable for all three logics. This paper corrects an error to the submitted version of Undecidability of Quantified Announcements, identified by Yuta Asami . The nature of the error was in the definition of the formula $cga(X)$ (see Subsection 5.2) which is corrected in this version., Comment: This paper contains a correction to the 2016 article, The Undecidablity of Quantified Announcements, published in Studia Logica

Although a few approaches are proposed to convert relational databases to graphs, there is a genuine lack of systematic evaluation across a wider spectrum of databases. Recognising the important issue of query mapping, this paper proposes an approach Rel2Graph, an automatic knowledge graph construction (KGC) approach from an arbitrary number of relational databases. Our approach also supports the mapping of conjunctive SQL queries into pattern-based NoSQL queries. We evaluate our proposed approach on two widely used relational database-oriented datasets: Spider and KaggleDBQA benchmarks for semantic parsing. We employ the execution accuracy (EA) metric to quantify the proportion of results by executing the NoSQL queries on the property knowledge graph we construct that aligns with the results of SQL queries performed on relational databases. Consequently, the counterpart property knowledge graph of benchmarks with high accuracy and integrity can be ensured. The code and data will be publicly available. The code and data are available at github\footnote{https://github.com/nlp-tlp/Rel2Graph}.

We present Q-Cogni, an algorithmically integrated causal reinforcement learning framework that redesigns Q-Learning with an autonomous causal structure discovery method to improve the learning process with causal inference. Q-Cogni achieves optimal learning with a pre-learned structural causal model of the environment that can be queried during the learning process to infer cause-and-effect relationships embedded in a state-action space. We leverage on the sample efficient techniques of reinforcement learning, enable reasoning about a broader set of policies and bring higher degrees of interpretability to decisions made by the reinforcement learning agent. We apply Q-Cogni on the Vehicle Routing Problem (VRP) and compare against state-of-the-art reinforcement learning algorithms. We report results that demonstrate better policies, improved learning efficiency and superior interpretability of the agent's decision making. We also compare this approach with traditional shortest-path search algorithms and demonstrate the benefits of our causal reinforcement learning framework to high dimensional problems. Finally, we apply Q-Cogni to derive optimal routing decisions for taxis in New York City using the Taxi & Limousine Commission trip record data and compare with shortest-path search, reporting results that show 85% of the cases with an equal or better policy derived from Q-Cogni in a real-world domain., Comment: 9 pages, 10 figures, 2 algorithms

Dynamic epistemic logics consider formal representations of agents' knowledge, and how the knowledge of agents changes in response to informative events, such as public announcements. Quantifying over informative events allows us to ask whether it is possible to achieve some state of knowledge, and has important applications in synthesising secure communication protocols. However, quantifying over quite simple informative events, public announcements, is not computable: such an arbitrary public announcement logic, APAL, has an undecidable satisfiability problem. Here we consider even simpler informative events called Boolean announcements, where announcements are restricted to be a Boolean combination of atomic propositions. The logic is called Boolean arbitrary public announcement logic, BAPAL. A companion paper provides a complete finitary axiomatization, and related expressivity results, for BAPAL. In this work the satisfiability problem for BAPAL is shown to decidable, and also that BAPAL does not have the finite model property.

Description logics are a powerful tool for describing ontological knowledge bases. That is, they give a factual account of the world in terms of individuals, concepts and relations. In the presence of uncertainty, such factual accounts are not feasible, and a subjective or epistemic approach is required. Aleatoric description logic models uncertainty in the world as aleatoric events, by the roll of the dice, where an agent has subjective beliefs about the bias of these dice. This provides a subjective Bayesian description logic, where propositions and relations are assigned probabilities according to what a rational agent would bet, given a configuration of possible individuals and dice. Aleatoric description logic is shown to generalise the description logic ALC, and can be seen to describe a probability space of interpretations of a restriction of ALC where all roles are functions. Several computational problems are considered and model-checking and consistency checking algorithms are presented. Finally, aleatoric description logic is shown to be able to model learning, where agents are able to condition their beliefs on the bias of dice according to observations., Comment: Short version submitted to DL2021

Quantification over public announcements shifts the perspective from reasoning strictly about the results of a particular announcement to reasoning about the existence of an announcement that achieves some certain epistemic goal. Depending on the type of the quantification, we get different formalisms, the most known of which are arbitrary public announcement logic (APAL), group announcement logic (GAL), and coalition announcement logic (CAL). It has been an open question whether the logics have the finite model property, and in the paper we answer the question negatively. We also discuss how this result is connected to other open questions in the field., Comment: In Proceedings TARK 2021, arXiv:2106.10886

We consider multi-agent systems where agents actions and beliefs are determined aleatorically, or "by the throw of dice". This system consists of possible worlds that assign distributions to independent random variables, and agents who assign probabilities to these possible worlds. We present a novel syntax and semantics for such system, and show that they generalise Modal Logic. We also give a sound and complete calculus for reasoning in the base semantics, and a sound calculus for the full modal semantics, that we conjecture to be complete. Finally we discuss some application to reasoning about game playing agents., Comment: Long version of paper accepted to appear at the 2019 Indian Conference on Logic and Applictaions

Arbitrary public announcement logic (APAL) reasons about how the knowledge of a set of agents changes after true public announcements and after arbitrary announcements of true epistemic formulas. We consider a variant of arbitrary public announcement logic called positive arbitrary public announcement logic (APAL+), which restricts arbitrary public announcements to announcement of positive formulas. Positive formulas prohibit statements about the ignorance of agents. The positive formulas correspond to the universal fragment in first-order logic. As two successive announcements of positive formulas need not correspond to the announcement of a positive formula, APAL+ is rather different from APAL. We show that APAL+ is more expressive than public announcement logic PAL, and that APAL+ is incomparable with APAL. We also provide a sound and complete infinitary axiomatisation.

Various extensions of public announcement logic have been proposed with quantification over announcements. The best-known extension is called arbitrary public announcement logic, APAL. It contains a primitive language construct Box phi intuitively expressing that "after every public announcement of a formula, formula phi is true". The logic APAL is undecidable and it has an infinitary axiomatization. Now consider restricting the APAL quantification to public announcements of Boolean formulas only, such that Box phi intuitively expresses that "after every public announcement of a Boolean formula, formula phi is true". This logic can therefore called Boolean arbitrary public announcement logic, BAPAL. The logic BAPAL is the subject of this work. Unlike APAL it has a finitary axiomatization. Also, BAPAL is not at least as expressive as APAL. A further claim that BAPAL is decidable is deferred to a companion paper.

Automata expressiveness is an essential feature in understanding which of the formalisms available should be chosen for modelling a particular problem. Probabilistic and stochastic automata are suitable for modelling systems exhibiting probabilistic behavior and their expressiveness has been studied relative to non-probabilistic transition systems and Markov chains. In this paper, we consider previous formalisms of Timed, Probabilistic and Stochastic Timed Automata, we present our new model of Timed Automata with Polynomial Delay, we introduce a measure of expressiveness for automata we call trace expressiveness and we characterize the expressiveness of these models relative to each other under this new measure., Comment: possibly incorrect, or unsubstantiated results and claims

We consider previous models of Timed, Probabilistic and Stochastic Timed Automata, we introduce our model of Timed Automata with Polynomial Delay and we characterize the expressiveness of these models relative to each other., Comment: possibly incorrect, or unsubstantiated results and claims

The paper presents a novel algorithm for computing best and worst case execution times (BCET/WCET) of timed automata models with cyclic behaviour. The algorithms can work on any arbitrary diagonal-free TA and can handle more cases than previously existing algorithms for BCET/WCET computations, as it can handle cycles in TA and decide whether they lead to an infinite WCET. We show soundness of the proposed algorithm and study its complexity. To our knowledge, this is the first model checking algorithm that addresses comprehensively the BCET/WCET problem of systems with cyclic behaviour. Behrmann et al. provide an algorithm for computing the minimum cost/time of reaching a goal state in priced timed automata (PTA). The algorithm has been implemented in the well-known model checking tool UPPAAL to compute the minimum time for termination of an automaton. However, we show that in certain circumstances, when infinite cycles exist, the algorithm implemented in UPPAAL may not terminate, and we provide examples which UPPAAL fails to verify.