What is modal logic

Autonomous AI systems need complex computational techniques for planning and performing actions. Planning and acting require significant deliberation because an intelligent system must coordinate and integrate these activities in order to act effectively in the real world. This book presents a comprehensive paradigm of planning and acting using the most recent and advanced automated-planning techniques. It explains the computational deliberation capabilities that allow an actor, whether physical or virtual, to reason about its actions, choose them, organize them purposefully, and act deliberately to achieve an objective. Useful for students, practitioners, and researchers, this book covers state-of-the-art planning techniques, acting techniques, and their integration which will allow readers to design intelligent systems that are able to act effectively in the real world.

Automated Planning and Acting

FF-Replan was the winner of the 2004 International Probabilistic Planning Competition (IPPC-04) (Younes & Littman 2004a) and was also the top performer on IPPC-06 domains, though it was not an official entry. This success was quite surprising, due to the simplicity of the approach. In particular, FF-Replan calls FF on a carefully constructed deterministic variant of the planning problem and selects actions according to the plan until observing an unexpected effect, upon which it replans. Despite the obvious shortcomings of the approach and its strawman nature, it is the state-of-the-art in probabilistic planning as measured on recent competition benchmarks. This paper gives the first technical description of FF-Replan and provides an analysis of its results on all of the recent IPPC-04 and IPPC-06 domains. We hope that this will inspire extensions and insight into the approach and planning domains themselves that will soon lead to the dethroning of FF-Replan.

/pdf/ff-replan-a-baseline-for-probabilistic-planning-47ba2cxf1n.pdf

FF-Replan: a baseline for probabilistic planning

An intelligent agent interacting with the real world will encounter individual people, courses, test results, drugs prescriptions, chairs, boxes, etc., and needs to reason about properties of these individuals and relations among them as well as cope with uncertainty. Uncertainty has been studied in probability theory and graphical models, and relations have been studied in logic, in particular in the predicate calculus and its extensions. This book examines the foundations of combining logic and probability into what are called relational probabilistic models. It introduces representations, inference, and learning techniques for probability, logic, and their combinations. The book focuses on two representations in detail: Markov logic networks, a relational extension of undirected graphical models and weighted first-order predicate calculus formula, and Problog, a probabilistic extension of logic programs that can also be viewed as a Turing-complete relational extension of Bayesian networks.

Statistical Relational Artificial Intelligence: Logic, Probability, and Computation

Markov Decision Processes (MDPs) are widely popular in Artificial Intelligence for modeling sequential decision-making scenarios with probabilistic dynamics. They are the framework of choice when designing an intelligent agent that needs to act for long periods of time in an environment where its actions could have uncertain outcomes. MDPs are actively researched in two related subareas of AI, probabilistic planning and reinforcement learning. Probabilistic planning assumes known models for the agent's goals and domain dynamics, and focuses on determining how the agent should behave to achieve its objectives. On the other hand, reinforcement learning additionally learns these models based on the feedback the agent gets from the environment. This book provides a concise introduction to the use of MDPs for solving probabilistic planning problems, with an emphasis on the algorithmic perspective. It covers the whole spectrum of the field, from the basics to state-of-the-art optimal and approximation algorithms. We first describe the theoretical foundations of MDPs and the fundamental solution techniques for them. We then discuss modern optimal algorithms based on heuristic search and the use of structured representations. A major focus of the book is on the numerous approximation schemes for MDPs that have been developed in the AI literature. These include determinization-based approaches, sampling techniques, heuristic functions, dimensionality reduction, and hierarchical representations. Finally, we briefly introduce several extensions of the standard MDP classes that model and solve even more complex planning problems. Table of Contents: Introduction / MDPs / Fundamental Algorithms / Heuristic Search Algorithms / Symbolic Algorithms / Approximation Algorithms / Advanced Notes

Planning with Markov Decision Processes: An AI Perspective

We present a heuristic search algorithm for solving first-order Markov Decision Processes (FOMDPs). Our approach combines first-order state abstraction that avoids evaluating states individually, and heuristic search that avoids evaluating all states. Firstly, in contrast to existing systems, which start with propositionalizing the FOMDP and then perform state abstraction on its propositionalized version we apply state abstraction directly on the FOMDP avoiding propositionalization. This kind of abstraction is referred to as first-order state abstraction. Secondly, guided by an admissible heuristic, the search is restricted to those states that are reachable from the initial state. We demonstrate the usefulness of the above techniques for solving FOMDPs with a system, referred to as FluCaP (formerly, FCPlanner), that entered the probabilistic track of the 2004 International Planning Competition (IPC'2004) and demonstrated an advantage over other planners on the problems represented in first-order terms.

https://www.aaai.org/Papers/JAIR/Vol27/JAIR-2712.pdf

FLUCAP: a heuristic search planner for first-order MDPs

We present a heuristic search algorithm for solving first-order Markov Decision Processes (FOMDPs). Our approach combines first-order state abstraction that avoids evaluating states individually, and heuristic search that avoids evaluating all states. Firstly, in contrast to existing systems, which start with propositionalizing the FOMDP and then perform state abstraction on its propositionalized version we apply state abstraction directly on the FOMDP avoiding propositionalization. This kind of abstraction is referred to as first-order state abstraction. Secondly, guided by an admissible heuristic, the search is restricted to those states that are reachable from the initial state. We demonstrate the usefulness of the above techniques for solving FOMDPs with a system, referred to as FluCaP (formerly, FCPlanner), that entered the probabilistic track of the 2004 International Planning Competition (IPC’2004) and demonstrated an advantage over other planners on the problems represented in first-order terms.

Engineering Note FluCaP: A Heuristic Search Planner for First-Order MDPs

We present a heuristic search algorithm for solving first-order MDPs (FOMDPs). Our approach combines first-order state abstraction that avoids evaluating states individually, and heuristic search that avoids evaluating all states. Firstly, we apply state abstraction directly on the FOMDP avoiding propositionalization. Such kind of abstraction is referred to as first-order state abstraction. Secondly, guided by an admissible heuristic, the search is restricted only to those states that are reachable from the initial state. We demonstrate the usefullness of the above techniques for solving FOMDPs on a system, referred to as FC-Planner, that entered the probabilistic track of the International Planning Competition (IPC'2004).

http://www.wv.inf.tu-dresden.de/Publications/2005/KarabaevSkvortsova-01.pdf

A heuristic search algorithm for solving first-order MDPs

Motivated by a chemical process engineering application, we introduce a new concept constructor in Description Logics (DLs), an n-ary variant of the existential restriction constructor, which generalizes both the usual existential restrictions and so-called qualified number restrictions We show that the new constructor can be expressed in , the extension of the basic DL by qualified number restrictions However, this representation results in an exponential blow-up By giving direct algorithms for extended with the new constructor, we can show that the complexity of reasoning in this new DL is actually not harder than the one of reasoning in  Moreover, in our chemical process engineering application, a restricted DL that provides only the new constructor together with conjunction, and satisfies an additional restriction on the occurrence of roles names, is sufficient For this DL, the subsumption problem is polynomial

A new n -ary existential quantifier in description logics

/pdf/mona-as-a-dl-reasoner-4ym12mr9gb.pdf

Eldar Karabaev

Papers

FLUCAP: a heuristic search planner for first-order MDPs

Engineering Note FluCaP: A Heuristic Search Planner for First-Order MDPs

A heuristic search algorithm for solving first-order MDPs

A new n -ary existential quantifier in description logics

Mona as a DL Reasoner.