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Knexus Research Corporation


Special Track on Case-Based Reasoning

AAAI Conferences

Case-based reasoning (CBR) is an artificial intelligence problem solving and learning methodology that retrieves and adapts previous experiences to fit newly encountered situations. This special track, currently in its 18th year, serves as an annual forum for researchers to present and discuss developments in CBR theory and application. Mirroring the annual International Conference on Case-Based Reasoning, this year’s special track has attracted a variety of high-quality submissions that present many valuable theoretical contributions and application domains. Although the CBR special track serves an important role as a focal point for the North American CBR community, this year continues the tradition of strong international participation. We would like to thank everyone who contributed to the success of this special track, especially the authors, the program committee members, the additional reviewers, and the FLAIRS conference organizers.


Kapetanakis

AAAI Conferences

Case-based reasoning (CBR) is an artificial intelligence problem solving and learning methodology that retrieves and adapts previous experiences to fit newly encountered situations. This special track, currently in its 18th year, serves as an annual forum for researchers to present and discuss developments in CBR theory and application. Mirroring the annual International Conference on Case-Based Reasoning, this year's special track has attracted a variety of high-quality submissions that present many valuable theoretical contributions and application domains. Although the CBR special track serves an important role as a focal point for the North American CBR community, this year continues the tradition of strong international participation. We would like to thank everyone who contributed to the success of this special track, especially the authors, the program committee members, the additional reviewers, and the FLAIRS conference organizers.


Towards Explainable NPCs: A Relational Exploration Learning Agent

AAAI Conferences

Non-player characters (NPCs) in video games are a common form of frustration for players because they generally provide no explanations for their actions or provide simplistic explanations using fixed scripts. Motivated by this, we consider a new design for agents that can learn about their environments, accomplish a range of goals, and explain what they are doing to a supervisor. We propose a framework for studying this type of agent, and compare it to existing reinforcement learning and self-motivated agent frameworks. We propose a novel design for an initial agent that acts within this framework. Finally, we describe an evaluation centered around the supervisor's satisfaction and understanding of the agent's behavior.


Molineaux

AAAI Conferences

Non-player characters (NPCs) in video games are a common form of frustration for players because they generally provide no explanations for their actions or provide simplistic explanations using fixed scripts. Motivated by this, we consider a new design for agents that can learn about their environments, accomplish a range of goals, and explain what they are doing to a supervisor. We propose a framework for studying this type of agent, and compare it to existing reinforcement learning and self-motivated agent frameworks. We propose a novel design for an initial agent that acts within this framework. Finally, we describe an evaluation centered around the supervisor's satisfaction and understanding of the agent's behavior.


Molineaux

AAAI Conferences

Human-agent teaming is a difficult yet relevant problem domain to which many goal reasoning systems are well suited, due to their ability to accept outside direction and (relatively) human-understandable internal state. We propose a formal model, and multiple variations on a multi-agent problem, to clarify and unify research in goal reasoning. We describe examples of these concepts, and propose standard evaluation methods for goal reasoning agents that act as a member of a team or on behalf of a supervisor.


Human-Agent Teaming as a Common Problem for Goal Reasoning

AAAI Conferences

Human-agent teaming is a difficult yet relevant problem domain to which many goal reasoning systems are well suited, due to their ability to accept outside direction and (relatively) human-understandable internal state. We propose a formal model, and multiple variations on a multi-agent problem, to clarify and unify research in goal reasoning. We describe examples of these concepts, and propose standard evaluation methods for goal reasoning agents that act as a member of a team or on behalf of a supervisor.


To

AAAI Conferences

Temporal logics have been used in autonomous planning to represent and reason about temporal planning problems. However, such techniques have typically been restricted to either (1) representing actions, events, and goals with temporal properties or (2) planning for temporally-extended goals under restrictive assumptions. We introduce Mixed Propositional Metric Temporal Logic (MPMTL) where formulae are built over mixed binary and continuous real variables. We introduce a planner, MTP, that solves MPMTL problems and includes a SAT-solver, model checker for a polynomial fragment of MPMTL, and a forward search algorithm. We extend PDDL 2.1 with MPMTL syntax to create MPDDL and an associated parser. The empirical study shows that MTP outperforms the state-of-the-art PDDL planner SMTPlan on several domains it performed best on and MTP performs and scales on problem size well for challenging domains with rich temporal properties we create.


A New Approach to Temporal Planning with Rich Metric Temporal Properties

AAAI Conferences

Temporal logics have been used in autonomous planning to represent and reason about temporal planning problems. However, such techniques have typically been restricted to either (1) representing actions, events, and goals with temporal properties or (2) planning for temporally-extended goals under restrictive assumptions. We introduce Mixed Propositional Metric Temporal Logic (MPMTL) where formulae are built over mixed binary and continuous real variables. We introduce a planner, MTP, that solves MPMTL problems and includes a SAT-solver, model checker for a polynomial fragment of MPMTL, and a forward search algorithm. We extend PDDL 2.1 with MPMTL syntax to create MPDDL and an associated parser. The empirical study shows that MTP outperforms the state-of-the-art PDDL+ planner SMTPlan+ on several domains it performed best on and MTP performs and scales on problem size well for challenging domains with rich temporal properties we create.


Shivashankar

AAAI Conferences

Heuristics serve as a powerful tool in modern domain-independent planning (DIP) systems by providing critical guidance during the search for high-quality solutions. However, they have not been broadly used with hierarchical planning techniques, which are more expressive and tend to scale better in complex domains by exploiting additional domain-specific knowledge. Complicating matters, we show that for Hierarchical Goal Network (HGN) planning, a goal-based hierarchical planning formalism that we focus on in this paper, any poly-time heuristic that is derived from a delete-relaxation DIP heuristic has to make some relaxation of the hierarchical semantics. To address this, we present a principled framework for incorporating DIP heuristics into HGN planning using a simple relaxation of the HGN semantics we call Hierarchy-Relaxation. This framework allows for computing heuristic estimates of HGN problems using any DIP heuristic in an admissibility-preserving manner. We demonstrate the feasibility of this approach by using the LMCut heuristic to guide an optimal HGN planner. Our empirical results with three benchmark domains demonstrate that simultaneously leveraging hierarchical knowledge and heuristic guidance substantially improves planning performance.


Incorporating Domain-Independent Planning Heuristics in Hierarchical Planning

AAAI Conferences

Heuristics serve as a powerful tool in modern domain-independent planning (DIP) systems by providing critical guidance during the search for high-quality solutions. However, they have not been broadly used with hierarchical planning techniques, which are more expressive and tend to scale better in complex domains by exploiting additional domain-specific knowledge. Complicating matters, we show that for Hierarchical Goal Network (HGN) planning, a goal-based hierarchical planning formalism that we focus on in this paper, any poly-time heuristic that is derived from a delete-relaxation DIP heuristic has to make some relaxation of the hierarchical semantics. To address this, we present a principled framework for incorporating DIP heuristics into HGN planning using a simple relaxation of the HGN semantics we call Hierarchy-Relaxation. This framework allows for computing heuristic estimates of HGN problems using any DIP heuristic in an admissibility-preserving manner. We demonstrate the feasibility of this approach by using the LMCut heuristic to guide an optimal HGN planner. Our empirical results with three benchmark domains demonstrate that simultaneously leveraging hierarchical knowledge and heuristic guidance substantially improves planning performance.