Dynamic Epistemic Logic (DEL) is a family of multimodal logics that has proved to be very successful for epistemic reasoning in planning tasks. In this logic, the agent's knowledge is captured by modal epistemic operators whereas the system evolution is described in terms of (some subset of) dynamic logic modalities in which actions are usually represented as semantic objects called event models. In this paper, we study a variant of DEL, that wecall DEL[ASP], where actions are syntactically described by using an Answer Set Programming (ASP) representation instead of event models. This representation directly inherits high level expressive features like indirect effects, qualifications, state constraints, defaults, or recursive fluents that are common in ASP descriptions of action domains. Besides, we illustrate how this approach can be applied for obtaining conditional plans in single-agent, partially observable domains where knowledge acquisition may be represented as indirect effects of actions.

We propose a solution to a time-varying variant of Markov Decision Processes which can be used to address decision-theoretic planning problems for autonomous systems operating in unstructured outdoor environments. We explore the time variability property of the planning stochasticity and investigate the state reachability, based on which we then develop an efficient iterative method that offers a good trade-off between solution optimality and time complexity. The reachability space is constructed by analyzing the means and variances of states' reaching time in the future. We validate our algorithm through extensive simulations using ocean data, and the results show that our method achieves a great performance in terms of both solution quality and computing time.

In order to be useful in the real world, AI agents need to plan and act in the presence of others, who may include adversarial and cooperative entities. In this paper, we consider the problem where an autonomous agent needs to act in a manner that clarifies its objectives to cooperative entities while preventing adversarial entities from inferring those objectives. We show that this problem is solvable when cooperative entities and adversarial entities use different types of sensors and/or prior knowledge. We develop two new solution approaches for computing such plans. One approach provides an optimal solution to the problem by using an IP solver to provide maximum obfuscation for adversarial entities while providing maximum legibility for cooperative entities in the environment, whereas the other approach provides a satisficing solution using heuristic-guided forward search to achieve preset levels of obfuscation and legibility for adversarial and cooperative entities respectively. We show the feasibility and utility of our algorithms through extensive empirical evaluation on problems derived from planning benchmarks.

In Multi-Agent Systems (MAS) there are two main models of interaction: among agents, and between agents and the environment. Although there are studies considering these models, there is no practical tool to afford the interaction with external entities with both models. This paper presents a proposal for such a tool based on the Apache Camel framework by designing two new components, namely camel-jason and camel-artifact. By means of these components, an external entity is modelled according to its nature, i.e., whether it is autonomous or non-autonomous, interacting with the MAS respectively as an agent or an artifact. It models coherently external entities whereas Camel provides interoperability with several communication protocols.

Nowadays, considering the speed of the processes and the amount of data used in cyber defense, it cannot be expected to have an effective defense by using only human power without the help of automation systems. However, for the effective defense against dynamically evolving attacks on networks, it is difficult to develop software with conventional fixed algorithms. This can be achieved by using artificial intelligence methods that provide flexibility and learning capability. The likelihood of developing cyber defense capabilities through increased intelligence of defense systems is quite high. Given the problems associated with cyber defense in real life, it is clear that many cyber defense problems can be successfully solved only when artificial intelligence methods are used. In this article, the current artificial intelligence practices and techniques are reviewed and the use and importance of artificial intelligence in cyber defense systems is mentioned. The aim of this article is to be able to explain the use of these methods in the field of cyber defense with current examples by considering and analyzing the artificial intelligence technologies and methodologies that are currently being developed and integrating them with the role and adaptation of the technology and methodology in the defense of cyberspace.

We develop model free PAC performance guarantees for multiple concurrent MDPs, extending recent works where a single learner interacts with multiple non-interacting agents in a noise free environment. Our framework allows noisy and resource limited communication between agents, and develops novel PAC guarantees in this extended setting. By allowing communication between the agents themselves, we suggest improved PAC-exploration algorithms that can overcome the communication noise and lead to improved sample complexity bounds. We provide a theoretically motivated algorithm that optimally combines information from the resource limited agents, thereby analyzing the interaction between noise and communication constraints that are ubiquitous in real-world systems. We present empirical results for a simple task that supports our theoretical formulations and improve upon naive information fusion methods.

Microsoft's latest breakthrough could make DNA-based data centers possible

Recent advances in deep reinforcement learning (RL) have shown remarkable success on challenging tasks (Andrychowicz et al., 2018; Mnih et al., 2015; Silver et al., 2016). However, data efficiency and robustness to new contexts remain persistent challenges for deep RL algorithms, especially when the goal is for agents to learn practical tasks with limited supervision. Drawing inspiration from self-supervised "play" in human development (Gopnik et al., 1999; Settles, 2011), we introduce an agent that learns object-centric representations of its environment without supervision and subsequently harnesses these to learn policies efficiency and robustly. Our agent, which we call Curious Object-Based seaRch Agent (COBRA), brings together three key ingredients: (i) learning representations of the world in terms of objects, (ii) curiosity-driven exploration, and (iii) model based RL. The benefits of this synthesis are data efficiency and policy robustness. To put this into practice, we introduce the following technical contributions: - A method for learning action-conditioned dynamics over slot-structured object-centric representations that requires no supervision and is trained from raw pixels.

We consider a blind identification problem in which we aim to recover a statistical model of a network without knowledge of the network's edges, but based solely on nodal observations of a certain process. More concretely, we focus on observations that consist of snapshots of a diffusive process that evolves over the unknown network. We model the network as generated from an independent draw from a latent stochastic block model (SBM), and our goal is to infer both the partition of the nodes into blocks, as well as the parameters of this SBM. We present simple spectral algorithms that provably solve the partition recovery and parameter estimation problems with high accuracy. Our analysis relies on recent results in random matrix theory and covariance estimation, and associated concentration inequalities. We illustrate our results with several numerical experiments.

Commitments capture how an agent relates to another agent, whereas goals describe states of the world that an agent is motivated to bring about. Commitments are elements of the social state of a set of agents whereas goals are elements of the private states of individual agents. It makes intuitive sense that goals and commitments are understood as being complementary to each other. More importantly, an agent's goals and commitments ought to be coherent, in the sense that an agent's goals would lead it to adopt or modify relevant commitments and an agent's commitments would lead it to adopt or modify relevant goals. However, despite the intuitive naturalness of the above connections, they have not been adequately studied in a formal framework. This article provides a combined operational semantics for goals and commitments by relating their respective life cycles as a basis for how these concepts (1) cohere for an individual agent and (2) engender cooperation among agents.