Brian Logan
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View article: Pushdown Reward Machines for Reinforcement Learning
Pushdown Reward Machines for Reinforcement Learning Open
Reward machines (RMs) are automata structures that encode (non-Markovian) reward functions for reinforcement learning (RL). RMs can reward any behaviour representable in regular languages and, when paired with RL algorithms that exploit RM…
View article: Probabilistic Strategy Logic with Degrees of Observability
Probabilistic Strategy Logic with Degrees of Observability Open
There has been considerable work on reasoning about the strategic ability of agents under imperfect information. However, existing logics such as Probabilistic Strategy Logic are unable to express properties relating to information transpa…
View article: Temporal Causal Reasoning with (Non-Recursive) Structural Equation Models
Temporal Causal Reasoning with (Non-Recursive) Structural Equation Models Open
Structural equation models (SEM) are a standard approach to representing causal dependencies between variables. In this paper we propose a new interpretation of existing formalisms in the field of Actual Causality in which SEM's are viewed…
View article: Temporal Causal Reasoning with (Non-Recursive) Structural Equation Models
Temporal Causal Reasoning with (Non-Recursive) Structural Equation Models Open
Structural Equation Models (SEM) are the standard approach to representing causal dependencies between variables in causal models. In this paper we propose a new interpretation of SEMs when reasoning about Actual Causality, in which SEMs a…
View article: Probabilistic Strategy Logic with Degrees of Observability
Probabilistic Strategy Logic with Degrees of Observability Open
There has been considerable work on reasoning about the strategic ability of agents under imperfect information. However, existing logics such as Probabilistic Strategy Logic are unable to express properties relating to information transpa…
View article: Maximally Permissive Reward Machines
Maximally Permissive Reward Machines Open
Reward machines allow the definition of rewards for temporally extended tasks and behaviors. Specifying “informative” reward machines can be challenging. One way to address this is to generate reward machines from a high-level abstract des…
View article: GenSynthPop: generating a spatially explicit synthetic population of individuals and households from aggregated data
GenSynthPop: generating a spatially explicit synthetic population of individuals and households from aggregated data Open
Synthetic populations are representations of actual individuals living in a specific area. They play an increasingly important role in studying and modeling individuals and are often used to build agent-based social simulations. Traditiona…
View article: GenSynthPop: Generating a Spatially Explicit Synthetic Population of Agents and Households from Aggregated Data
GenSynthPop: Generating a Spatially Explicit Synthetic Population of Agents and Households from Aggregated Data Open
Synthetic populations are representations of actual individuals living in a specific area. They play an increasingly important role in studying and modeling individuals and are often used to build agent-based social simulations. Traditiona…
View article: GenSynthPop: Generating a Spatially Explicit Synthetic Population of Individuals and Households from Aggregated Data
GenSynthPop: Generating a Spatially Explicit Synthetic Population of Individuals and Households from Aggregated Data Open
Synthetic populations are representations of actual individuals living in a specificarea. They play an increasingly important role in studying and modeling individuals and are often used to build agent-based social simulations. Traditional…
View article: Maximally Permissive Reward Machines
Maximally Permissive Reward Machines Open
Reward machines allow the definition of rewards for temporally extended tasks and behaviors. Specifying "informative" reward machines can be challenging. One way to address this is to generate reward machines from a high-level abstract des…
View article: Quantifying the Self-Interest Level of Markov Social Dilemmas
Quantifying the Self-Interest Level of Markov Social Dilemmas Open
This paper introduces a novel method for estimating the self-interest level of Markov social dilemmas. We extend the concept of self-interest level from normal-form games to Markov games, providing a quantitative measure of the minimum rew…
View article: Pure-Past Action Masking
Pure-Past Action Masking Open
We present Pure-Past Action Masking (PPAM), a lightweight approach to action masking for safe reinforcement learning. In PPAM, actions are disallowed (“masked”) according to specifications expressed in Pure-Past Linear Temporal Logic (PPLT…
View article: GenSynthPop: Generating a Spatially Explicit Synthetic Population of Agents and Households from Aggregated Data
GenSynthPop: Generating a Spatially Explicit Synthetic Population of Agents and Households from Aggregated Data Open
Synthetic populations are microscopic representations of actual citizens living in a specific area. They play an increasingly important role in studying and modeling citizens and are often used to build agent-based social simulations.Tradi…
View article: Dynamic Causality
Dynamic Causality Open
There have been a number of attempts to develop a formal definition of causality that accords with our intuitions about what constitutes a cause. Perhaps the best known is the “modified” definition of actual causality, HPm, due to Halpern.…
View article: A framework for modeling human behavior in large-scale agent-based epidemic simulations
A framework for modeling human behavior in large-scale agent-based epidemic simulations Open
Agent-based modeling is increasingly being used in computational epidemiology to characterize important behavioral dimensions, such as the heterogeneity of the individual responses to interventions, when studying the spread of a disease. E…
View article: Data-Driven Revision of Conditional Norms in Multi-Agent Systems (Extended Abstract)
Data-Driven Revision of Conditional Norms in Multi-Agent Systems (Extended Abstract) Open
In multi-agent systems, norm enforcement is a mechanism for steering the behavior of individual agents in order to achieve desired system-level objectives. Due to the dynamics of multi-agent systems, however, it is hard to design norms tha…
View article: Probabilistic Temporal Logic for Reasoning about Bounded Policies
Probabilistic Temporal Logic for Reasoning about Bounded Policies Open
To build a theory of intention revision for agents operating in stochastic environments, we need a logic in which we can explicitly reason about their decision-making policies and those policies' uncertain outcomes. Towards this end, we pr…
View article: Multi-Agent Intention Recognition and Progression
Multi-Agent Intention Recognition and Progression Open
For an agent in a multi-agent environment, it is often beneficial to be able to predict what other agents will do next when deciding how to act. Previous work in multi-agent intention scheduling assumes a priori knowledge of the current go…
View article: A Logic of East and West
A Logic of East and West Open
We propose a logic of east and west (LEW ) for points in 1D Euclidean space. It formalises primitive direction relations: east (E), west (W) and indeterminate east/west (Iew). It has a parameter τ ∈ N>1, which is referred to as the level o…
View article: Data-Driven Revision of Conditional Norms in Multi-Agent Systems
Data-Driven Revision of Conditional Norms in Multi-Agent Systems Open
In multi-agent systems, norm enforcement is a mechanism for steering the behavior of individual agents in order to achieve desired system-level objectives. Due to the dynamics of multi-agent systems, however, it is hard to design norms tha…
View article: Resilience, reliability, and coordination in autonomous multi-agent systems
Resilience, reliability, and coordination in autonomous multi-agent systems Open
Multi-agent systems is an evolving discipline that encompasses many different branches of research. The long-standing Agents at Aberdeen ( A 3 ) group undertakes research across several areas of multi-agent systems, focusing in particular …
View article: Situation Calculus for Controller Synthesis in Manufacturing Systems with First-Order State Representation (Extended Abstract)
Situation Calculus for Controller Synthesis in Manufacturing Systems with First-Order State Representation (Extended Abstract) Open
Manufacturing is transitioning from a mass production model to a service model in which facilities `bid' for previously unseen products. To decide whether to bid for a previously unseen product, a facility must be able to synthesize, on th…
View article: Multi-Agent Intention Progression with Reward Machines
Multi-Agent Intention Progression with Reward Machines Open
Recent work in multi-agent intention scheduling has shown that enabling agents to predict the actions of other agents when choosing their own actions can be beneficial. However existing approaches to 'intention-aware' scheduling assume tha…
View article: Automatic Synthesis of Dynamic Norms for Multi-Agent Systems
Automatic Synthesis of Dynamic Norms for Multi-Agent Systems Open
Norms have been widely proposed to coordinate and regulate multi-agent systems (MAS) behaviour. We consider the problem of synthesising and revising the set of norms in a normative MAS to satisfy a design objective expressed in Alternating…
View article: The Complexity of Data-Driven Norm Synthesis and Revision
The Complexity of Data-Driven Norm Synthesis and Revision Open
Norms have been widely proposed as a way of coordinating and controlling the activities of agents in a multi-agent system (MAS). A norm specifies the behaviour an agent should follow in order to achieve the objective of the MAS. However, d…
View article: Multi-Agent Intention Progression with Black-Box Agents
Multi-Agent Intention Progression with Black-Box Agents Open
We propose a new approach to intention progression in multi-agent settings where other agents are effectively black boxes. That is, while their goals are known, the precise programs used to achieve these goals are not known. In our approac…
View article: Belief revision and dialogue management in information retrieval
Belief revision and dialogue management in information retrieval Open
This report describes research to evaluate a theory of belief revision proposed by Galliers in the context of information-seeking interaction as modelled by Belkin, Brooks and Daniels and illustrated by user-librarian dialogues. The work c…