Reasoning about the executability of goal-plan trees

User supplied domain control knowledge in the form of hierarchically structured agent plans is at the heart of a number of approaches to reasoning about action. This knowledge encodes the “standard operating procedures” of an agent for responding to environmental changes, thereby enabling fast and effective action selection. This paper develops mechanisms for reasoning about a set of hierarchical plans and goals, by deriving “summary information” from the conditions on the execution of the basic actions forming the “leaves” of the hierarchy. We provide definitions of necessary and contingent pre-, in-, and postconditions of goals and plans that are consistent with the conditions of the actions forming a plan. Our definitions extend previous work with an account of both deterministic and non-deterministic actions, and with support for specifying that actions and goals within a (single) plan can execute concurrently. Based on our new definitions, we also specify requirements that are useful in scheduling the execution of steps in a set of goal-plan trees. These requirements essentially define conditions that must be protected by any scheduler that interleaves the execution of steps from different goal-plan trees

\u3ci\u3eCorrect Reasoning: Essays on Logic-Based AI in Honour of Vladimir Lifschitz\u3c/i\u3e

Co-edited by Yuliya Lierler, UNO faculty member. Essay, Parsing Combinatory Categorial Grammar via Planning in Answer Set Programming, co-authored by Yuliya Lierler, UNO faculty member. This Festschrift published in honor of Vladimir Lifschitz on the occasion of his 65th birthday presents 39 articles by colleagues from all over the world with whom Vladimir Lifschitz had cooperation in various respects. The 39 contributions reflect the breadth and the depth of the work of Vladimir Lifschitz in logic programming, circumscription, default logic, action theory, causal reasoning and answer set programming.https://digitalcommons.unomaha.edu/facultybooks/1231/thumbnail.jp

Progressing intention progression: a call for a Goal-Plan Tree contest

User-supplied domain control knowledge in the form of hierarchically structured Goal-Plan Trees (GPTs) is at the heart of a number of approaches to reasoning about action. Reasoning with GPTs connects the AAMAS community with other communities such as automated planning, and forms the foundation for important reasoning capabilities, especially intention progression in Belief-Desire-Intention (BDI) agents. Research on GPTs has a long history but suffers from fragmentation and lack of common terminology, data formats, and enabling tools. One way to address this fragmentation is through a competition. Competitions are increasingly being used as a means to foster research and challenge the state of the art. For example, the AAMAS conference has a number of associated competitions, such as the Trading Agent Competition, while agent research is showcased at competitions such as RoboCup. We therefore issue a call for a Goal-Plan Tree Contest, with the ambition of drawing together a community and incentivizing research in intention progression

Generating Executable Action Plans with Environmentally-Aware Language Models

Large Language Models (LLMs) trained using massive text datasets have recently shown promise in generating action plans for robotic agents from high level text queries. However, these models typically do not consider the robot's environment, resulting in generated plans that may not actually be executable, due to ambiguities in the planned actions or environmental constraints. In this paper, we propose an approach to generate environmentally-aware action plans that agents are better able to execute. Our approach involves integrating environmental objects and object relations as additional inputs into LLM action plan generation to provide the system with an awareness of its surroundings, resulting in plans where each generated action is mapped to objects present in the scene. We also design a novel scoring function that, along with generating the action steps and associating them with objects, helps the system disambiguate among object instances and take into account their states. We evaluated our approach using the VirtualHome simulator and the ActivityPrograms knowledge base and found that action plans generated from our system had a 310% improvement in executability and a 147% improvement in correctness over prior work. The complete code and a demo of our method is publicly available at https://github.com/hri-ironlab/scene_aware_language_planner

Intentions in Means-End Planning (Dissertation Proposal)

This proposal discusses the use of the intentions of the actor in performing means-end reasoning. In doing so, it will show that preconditions and applicability conditions in existing systems are ill-defined and intrinsically encode situational information that prevents intentions from playing a role in the planning process. While the former problem can be fixed, the latter cannot. Therefore, I argue that preconditions should be eliminated from action representation. In their place, I suggest explicit representation of intention, situated reasoning about the results of action, and robust failure mechanisms. I then describe a system, the Intentional Planning System (ItPlanS), which embodies these ideas, compare ItPlanS to other systems, and propose future directions for this work

Action, Time and Space in Description Logics

Description Logics (DLs) are a family of logic-based knowledge representation (KR) formalisms designed to represent and reason about static conceptual knowledge in a semantically well-understood way. On the other hand, standard action formalisms are KR formalisms based on classical logic designed to model and reason about dynamic systems. The largest part of the present work is dedicated to integrating DLs with action formalisms, with the main goal of obtaining decidable action formalisms with an expressiveness significantly beyond propositional. To this end, we offer DL-tailored solutions to the frame and ramification problem. One of the main technical results is that standard reasoning problems about actions (executability and projection), as well as the plan existence problem are decidable if one restricts the logic for describing action pre- and post-conditions and the state of the world to decidable Description Logics. A smaller part of the work is related to decidable extensions of Description Logics with concrete datatypes, most importantly with those allowing to refer to the notions of space and time