This paper explores the challenge of encountering incorrect beliefs in the context of reasoning about actions and changes using action languages with sensing actions. An incorrect belief occurs when some observations conflict with the agent’s own beliefs. A common approach to recover from this situation is to replace the initial beliefs with beliefs that conform to the sequence of actions and the observations. The paper introduces a regression-based and revision-based approach to calculate a correct initial belief. Starting from an inconsistent history consisting of actions and observations, the proposed framework (1) computes the initial belief states that support the actions and observations and (2) uses a belief revision operator to repair the false initial belief state. The framework operates on domains with static causal laws, supports arbitrary sequences of actions, and integrates belief revision methods to select a meaningful initial belief state among possible alternatives.
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A Logic Programming Approach to Regression Based Repair of Incorrect Initial Belief States
This paper introduces a combination of regression and belief revision to allow agents to deal with inconsistencies while executing plans. Starting from an inconsistent history consisting
of actions and observations, the proposed framework (1) computes the initial belief states that support the actions and observations and (2) uses a belief revision operator to repair the false initial belief state. The framework operates on domains with static causal laws and supports arbitrary sequences of actions. The paper illustrates how logic programming can be effectively used to support these processes.
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- Award ID(s):
- 1914635
- NSF-PAR ID:
- 10208823
- Date Published:
- Journal Name:
- International Conference on Logic Programming
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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The paper proposes a framework for capturing how an agent’s beliefs evolve over time in response to observations and for answering the question of whether statements made by a third party can be believed. The basic components of the framework are a formalism for reasoning about actions, changes, and observations and a formalism for default reasoning. The paper describes a concrete implementation that leverages answer set programming for determining the evolution of an agent's ``belief state'', based on observations, knowledge about the effects of actions, and a theory about how these influence an agent's beliefs. The beliefs are then used to assess whether statements made by a third party can be accepted as truthful. The paper investigates an application of the proposed framework in the detection of man-in-the-middle attacks targeting computers and cyber-physical systems. Finally, we briefly discuss related work and possible extensions.
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Can a lack of transitional justice contribute to democratic backsliding? This paper uses the case of Poland to argue that selective enforcement of transitional justice can be linked to democratic erosion. In doing so, the paper adjudicates between two theories of democratic backsliding. The first, advanced by Milan Svolik, argues that elite polarization drives erosion: when political candidates are ideologically far apart, citizens who strongly prefer one over the other may turn a blind eye to antidemocratic transgressions by their preferred candidate to prevent the competing candidate from winning. The second theory, presented by Nalepa, Vanberg, and Ciopris (NVC), describes an equilibrium where voters are uncertain whether the candidate they are dealing with is a closet autocrat or an ideological incumbent, but reelect him into office regardless. This theory posits that a closet autocrat is reelected into office because his first period actions are identical to those of an ideological incumbent. I argue that judiciary reforms in Poland reflect exactly the kind of incumbent actions that are consistent both with the actions of an ideological incumbent and with the actions of a closet autocrat. Using survey data from Poland, I find evidence of elite polarization, offering support for the first theory, but also find ample evidence of polarization in the electorate and of a belief structure supportive of the equilibrium from NVC. I present Hungary’s experience with transitional justice and the rule of law as a shadow case to illustrate similar dynamics to those taking place in Poland.more » « less
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Abstract This paper presents an implementation of Connected Spaces (CxS)—an ambient help seeking interface designed and developed for a project‐based computing classroom. We use actor network theory (ANT) to provide an underutilized posthumanist lens to understand the creation of collaborative connections in this Computational Action‐based implementation. Posthumanism offers an emerging and critical extension to sociocultural perspectives on understanding learning, by pushing us to decenter the human, and consider the active roles that human and non‐human entities play in learning environments by actively shaping each other. We analyse how students in this class adjusted their help‐seeking and collaborative habits following the introduction of CxS, a tool designed to foster (more inter‐group) collaboration. ANT proposes generalized symmetry—a principle of considering human, non‐human and more than human entities with equivalent and comparable agency, leading to describing phenomena as networks of actors in different evolving relationships with each other. Analysing collaborative interactions as fostered by CxS using an ANT approach supports design‐based research—an iterative design revision process highlighting understandings about design as well as learning—by providing a temporal and informative lens into the relationship between actors and tools within the environment. Our key findings include a framing of technologies in classrooms as bridging
agentic gaps between students and becoming actors engaging in different behaviours; learners enacting new agencies through technologies (for instance a more comfortable non‐intrusive help seeker), and the need for voicing and teachers to connect help networks in CxS equipped classrooms.Practitioner notes What is already known about this topic
Collaborative learning is a valuable skill and practice; opportunities to mentor others are critical in empowering minoritized learners, especially in STEM and computing disciplines.
School norms solidify a power and expertise hierarchy between teachers and learners and fail to productively support learners in learning from each other.
Additionally, lack of awareness about peers' knowledge is a common hindrance in students knowing who to ask for help and how.
What this paper adds
An example of a designed interface called Connected Spaces with potential to foster more inter‐student collaboration, especially outside of mandated within‐group collaboration—in the form of cross‐group help seeking and help giving.
A design based research study using actor network theory highlighting the limitations of Connected Spaces in sparking notable behaviour change among students by itself but being retooled as a teacher support tool in enabling cross‐group collaborations.
Presenting conceptions of collaboration through technologies as bridging agentic gaps and acting with new agencies in performing help‐seeking related actions.
Provoking the idea of testing emerging technologies in classrooms along with sharing our analyses and reflections with the classroom as a key idea in computing education—surfacing the gap between designed intentions and the different kinds of extra social work needed in the on‐ground success of different technologies.
Implications for practice and/or policy
Designers and researchers should create and test more interfaces alongside teachers across different classrooms and contexts aimed at supporting different kinds of voluntary collaborative interactions.
Curricula, standards and school practices should further center providing students with opportunities to engage as mentors and build communities of learning across disciplines to empower minoritized students.
Researchers engaging in design based research should consider using more posthumanist lenses to examine educational technologies and how they affect change in learning environments.
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We address the problem of robot motion planning under uncertainty where the only observations are through contact with the environment. Such problems are typically solved by planning optimistically assuming unknown space is free, moving along the planned path and re-planning if the robot collides. However this approach can be very inefficient, leading to many unnecessary collisions and unproductive motion. We propose a new formulation, the Blindfolded Traveler’s Problem (BTP), for planning on a graph containing edges with unknown validity, with true validity observed only through attempted traversal by the robot. The solution to a BTP is a policy indicating the next edge to attempt given previous observations and an initial belief. We prove that BTP is NP-complete and show that exact modeling of the belief is intractable, therefore we present several approximation-based policies and beliefs. For the policy we propose graph search with edge weights augmented by the probability of collision. For the belief representation we propose a weighted Mixture of Experts of Collision Hypothesis Sets and a Manifold Particle Filter. Empirical evaluation in simulation and on a real robot arm shows that our proposed approach vastly outperforms several baselines as well as a previous approach that does not employ the BTP framework.
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