A Semantic Approach to Decidability in Epistemic Planning (Extended Version)

The use of Dynamic Epistemic Logic (DEL) in multi-agent planning has led to a widely adopted action formalism that can handle nondeterminism, partial observability and arbitrary knowledge nesting. As such expressive power comes at the cost of undecidability, several decidable fragments have been isolated, mainly based on syntactic restrictions of the action formalism. In this paper, we pursue a novel semantic approach to achieve decidability. Namely, rather than imposing syntactical constraints, the semantic approach focuses on the axioms of the logic for epistemic planning. Specifically, we augment the logic of knowledge S5$_n$ and with an interaction axiom called (knowledge) commutativity, which controls the ability of agents to unboundedly reason on the knowledge of other agents. We then provide a threefold contribution. First, we show that the resulting epistemic planning problem is decidable. In doing so, we prove that our framework admits a finitary non-fixpoint characterization of common knowledge, which is of independent interest. Second, we study different generalizations of the commutativity axiom, with the goal of obtaining decidability for more expressive fragments of DEL. Finally, we show that two well-known epistemic planning systems based on action templates, when interpreted under the setting of knowledge, conform to the commutativity axiom, hence proving their decidability

Faster Optimal State-Space Search with Graph Decomposition and Reduced Expansion

Traditional AI search methods, such as BFS, DFS, and A*, look for a path from a starting state to the goal in a state space most typically modelled as a directed graph. Prohibitively large sizes of the state space graphs make optimal search difficult. A key observation, as manifested by the SAS+ formalism for planning, is that most commonly a state-space graph is well structured as the Cartesian product of several small subgraphs. This paper proposes novel search algorithms that exploit such structure. The results reveal that standard search algorithms may explore many redundant paths. Our algorithms provide an automatic and mechanical way to remove such redundancy. Theoretically we prove the optimality and complexity reduction of the proposed algorithms. We further show that the proposed framework can accommodate classical planning. Finally, we evaluate our algorithms on various planning domains and report significant complexity reduction

Hierarchical Goal Networks: Formalisms and Algorithms for Planning and Acting

In real-world applications of AI and automation such as in robotics, computer game playing and web-services, agents need to make decisions in unstructured environments that are open-world, dynamic and partially observable. In the AI and Robotics research communities in particular, there is much interest in equipping robots to operate with minimal human intervention in diverse scenarios such as in manufacturing plants, homes, hospitals, etc. Enabling agents to operate in these environments requires advanced planning and acting capabilities, some of which are not well supported by the current state of the art automated planning formalisms and algorithms. To address this problem, in my thesis I propose a new planning formalism that addresses some of the inadequacies in current planning frameworks, and a suite of planning and acting algorithms that operate under this planning framework. The main contributions of this thesis are: - Hierarchical Goal Network (HGN) Planning Formalism. This planning formalism combines aspects (and therefore harnesses advantages) of Classical Planning and Hierarchical Task Network (HTN) Planning, two of the most prominent planning formalisms currently in use. In particular, HGN planning algorithms, while retaining the efficiency and scalability advantages of HTNs, also allows incorporation of heuristics and other reasoning techniques from Classical Planning. - Planning Algorithms. Goal Decomposition Planner (GDP) and the Goal Decomposition with Landmarks (GoDeL) planner are two HGN planning algorithms that combines hierarchical decomposition with classical planning heuristics to outperform state-of-the-art HTN planners like SHOP and SHOP2. - Integration with Robotics. The Combined HGN and Motion Planning (CHaMP) algorithm integrates GoDeL with low-level motion and manipulation planning algorithms in Robotics to generate plans directly executable by robots. Given the need for autonomous agents to operate in open, dynamic and unstructured environments and the obvious need for high-level deliberation capabilities to enable intelligent behavior, the planning-and-acting systems that are developed as part of this thesis may provide unique insights into ways to realize these systems in the real world

A Gentle Introduction to Epistemic Planning: The DEL Approach

Epistemic planning can be used for decision making in multi-agent situations with distributed knowledge and capabilities. Dynamic Epistemic Logic (DEL) has been shown to provide a very natural and expressive framework for epistemic planning. In this paper, we aim to give an accessible introduction to DEL-based epistemic planning. The paper starts with the most classical framework for planning, STRIPS, and then moves towards epistemic planning in a number of smaller steps, where each step is motivated by the need to be able to model more complex planning scenarios.Comment: In Proceedings M4M9 2017, arXiv:1703.0173

A distributed knowledge-based approach to flexible automation : the contract-net framework

Includes bibliographical references (p. 26-29)

Narrative based Postdictive Reasoning for Cognitive Robotics

Making sense of incomplete and conflicting narrative knowledge in the presence of abnormalities, unobservable processes, and other real world considerations is a challenge and crucial requirement for cognitive robotics systems. An added challenge, even when suitably specialised action languages and reasoning systems exist, is practical integration and application within large-scale robot control frameworks. In the backdrop of an autonomous wheelchair robot control task, we report on application-driven work to realise postdiction triggered abnormality detection and re-planning for real-time robot control: (a) Narrative-based knowledge about the environment is obtained via a larger smart environment framework; and (b) abnormalities are postdicted from stable-models of an answer-set program corresponding to the robot's epistemic model. The overall reasoning is performed in the context of an approximate epistemic action theory based planner implemented via a translation to answer-set programming.Comment: Commonsense Reasoning Symposium, Ayia Napa, Cyprus, 201