A Theory of Intentions for Intelligent Agents

Blount, Justin; Gelfond, Michael; Balduccini, Marcello

doi:10.1007/978-3-319-23264-5_12

Cited by 16 publications

(24 citation statements)

References 4 publications

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“…This architecture did not model agents with specific goals, but it was used to enable an observer to recognize an agent's activity and intention [12]. The Theory of Intentions (T I) extended this work to goal-driven agents by expanding transition diagrams with physical states and physically executable actions to include mental fluents and actions [4,5]. It associated a sequence of agent actions (called an "activity") with the goal it intended to achieve, and the intentional agent only performed activities needed to achieve the goal.…”

Section: Related Workmentioning

confidence: 99%

“…In the latter case, the robot will attempt to explain the unexpected outcome (i.e., perform diagnostics) and compute a new plan if necessary. To motivate the need for a different approach in dynamic domains, consider the following five scenarios in which the goal is to move book 1 and book 2 to the library; these scenarios have been adapted from scenarios considered in prior work [5]:…”

Section: Adapted Theory Of Intentionmentioning

confidence: 99%

“…First, the signature c is expanded to represent an activity as a triplet comprising a goal, a plan to achieve the goal, and a specific name for the activity. We do so by introducing (in c ) relations such as: activity(name), activity goal(name, goal) (5) activity length(name, length) activity component (name, number, action) which represent each named activity, the goal and length of each activity, and the actions that are the components of the activity. Note that these relations are not ground initially because the specific activities and goals are constructed or defined as needed.…”

Section: Adapted Theory Of Intentionmentioning

confidence: 99%

“…1 Furthermore, for the execution of each concrete action, we use existing algorithms that include probabilistic models of the uncertainty in perception and actuation. Our architecture builds on the complementary strengths of prior work on an architecture that used declarative programming to reason about intended actions to achieve a given goal [5], and an architecture that introduced step-wise refinement of tightly-coupled transition diagrams at two different resolutions to support non-monotonic logical reasoning and probabilistic reasoning for planning and diagnostics [26]. Prior work on the refinementbased architecture did not include a theory of intentions.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

What do you really want to do? Towards a Theory of Intentions for Human-Robot Collaboration

Gómez

Sridharan

Riley

2020

Ann Math Artif Intell

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The architecture described in this paper encodes a theory of intentions based on the key principles of non-procrastination, persistence, and automatically limiting reasoning to relevant knowledge and observations. The architecture reasons with transition diagrams of any given domain at two different resolutions, with the fine-resolution description defined as a refinement of, and hence tightly-coupled to, a coarse-resolution description. For any given goal, nonmonotonic logical reasoning with the coarse-resolution description computes an activity, i.e., a plan, comprising a sequence of abstract actions to be executed to achieve the goal. Each abstract action is implemented as a sequence of concrete actions by automatically zooming to and reasoning with the part of the fine-resolution transition diagram relevant to the current coarse-resolution transition and the goal. Each concrete action in this sequence is executed using probabilistic models of the uncertainty in sensing and actuation, and the corresponding fine-resolution outcomes are used to infer coarse-resolution observations that are added to the coarse-resolution history. The architecture's capabilities are evaluated in the context of a simulated robot assisting humans in an office domain, on a physical robot (Baxter) manipulating tabletop objects, and on a wheeled robot (Turtlebot) moving objects to particular places or people. The experimental results indicate improvements in reliability and computational efficiency compared with an architecture that does not include the theory of intentions, and an architecture that does not include zooming for fine-resolution reasoning.

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Section: Related Workmentioning

confidence: 99%

Section: Adapted Theory Of Intentionmentioning

confidence: 99%

Section: Adapted Theory Of Intentionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

What do you really want to do? Towards a Theory of Intentions for Human-Robot Collaboration

Gómez

Sridharan

Riley

2020

Ann Math Artif Intell

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“…Blount et al (Blount 2013;Blount et al 2015) developed a theory about the intentions of a goal-driven agent by substantially elaborating on previous work by Baral and Gelfond (2005). In their theory, each sequence of actions (i.e., plan) of an agent was associated with a goal that it was meant to achieve, and the combination of the two was called an activity.…”

Section: Preliminary: Theory Of Intentionsmentioning

confidence: 99%

An ASP Methodology for Understanding Narratives about Stereotypical Activities

Inclezan¹,

Balduccini²,

Israney³

2018

Theory and Practice of Logic Programming

Self Cite

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We describe an application of Answer Set Programming to the understanding of narratives about stereotypical activities, demonstrated via question answering. Substantial work in this direction was done by Erik Mueller, who modeled stereotypical activities as scripts. His systems were able to understand a good number of narratives, but could not process texts describing exceptional scenarios. We propose addressing this problem by using a theory of intentions developed by Blount, Gelfond, and Balduccini. We present a methodology in which we substitute scripts by activities (i.e., hierarchical plans associated with goals) and employ the concept of an intentional agent to reason about both normal and exceptional scenarios. We exemplify the application of this methodology by answering questions about a number of restaurant stories. This paper is under consideration for acceptance in TPLP.

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A survey of knowledge‐based sequential decision‐making under uncertainty

Zhang

Sridharan

2022

AI Magazine

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Reasoning with declarative knowledge (RDK) and sequential decision-making (SDM) are two key research areas in artificial intelligence. RDK methods reason with declarative domain knowledge, including commonsense knowledge, that is either provided a priori or acquired over time, while SDM methods (probabilistic planning [PP] and reinforcement learning [RL]) seek to compute action policies that maximize the expected cumulative utility over a time horizon; both classes of methods reason in the presence of uncertainty. Despite the rich literature in these two areas, researchers have not fully explored their complementary strengths.In this paper, we survey algorithms that leverage RDK methods while making sequential decisions under uncertainty. We discuss significant developments, open problems, and directions for future work.

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A Theory of Intentions for Intelligent Agents

Cited by 16 publications

References 4 publications

What do you really want to do? Towards a Theory of Intentions for Human-Robot Collaboration

What do you really want to do? Towards a Theory of Intentions for Human-Robot Collaboration

An ASP Methodology for Understanding Narratives about Stereotypical Activities

A survey of knowledge‐based sequential decision‐making under uncertainty

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