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Brian Logan 0001

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14 papers
1 author row

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14

ECAI Conference 2024 Conference Paper

Maximally Permissive Reward Machines

  • Giovanni Varricchione
  • Natasha Alechina
  • Mehdi Dastani
  • Brian Logan 0001

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 description of the learning environment, using techniques such as AI planning. However, previous planning-based approaches generate a reward machine based on a single (sequential or partial-order) plan, and do not allow maximum flexibility to the learning agent. In this paper we propose a new approach to synthesising reward machines which is based on the set of partial order plans for a goal. We prove that learning using such “maximally permissive” reward machines results in higher rewards than learning using RMs based on a single plan. We present experimental results which support our theoretical claims by showing that our approach obtains higher rewards than the single-plan approach in practice.

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ECAI Conference 2023 Conference Paper

Dynamic Causality

  • Maksim Gladyshev
  • Natasha Alechina
  • Mehdi Dastani
  • Dragan Doder
  • Brian Logan 0001

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. In this paper, we argue that HPm gives counterintuitive results for some simple causal models. We propose Dynamic Causality (DC), an alternative semantics for causal models that leads to an alternative definition of causes. DC ascribes the same causes as HPm on the examples of causal models widely discussed in the literature and ascribes intuitive causes for the kinds of causal models we consider. Moreover, we show that the complexity of determining a cause under the DC definition is lower than for the HPm definition.

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EUMAS Conference 2023 Conference Paper

Synthesising Reward Machines for Cooperative Multi-Agent Reinforcement Learning

  • Giovanni Varricchione
  • Natasha Alechina
  • Mehdi Dastani
  • Brian Logan 0001

Abstract Reward machines have recently been proposed as a means of encoding team tasks in cooperative multi-agent reinforcement learning. The resulting multi-agent reward machine is then decomposed into individual reward machines, one for each member of the team, allowing agents to learn in a decentralised manner while still achieving the team task. However, current work assumes the multi-agent reward machine to be given. In this paper, we show how reward machines for team tasks can be synthesised automatically from an Alternating-Time Temporal Logic specification of the desired team behaviour and a high-level abstraction of the agents’ environment. We present results suggesting that our automated approach has comparable, if not better, sample efficiency than reward machines generated by hand for multi-agent tasks.

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ECAI Conference 2016 Conference Paper

Realisability of Production Recipes

  • Lavindra de Silva
  • Paolo Felli
  • Jack C. Chaplin
  • Brian Logan 0001
  • David Sanderson
  • Svetan M. Ratchev

There is a rising demand for customised products with a high degree of complexity. To meet these demands, manufacturing lines are increasingly becoming autonomous, networked, and intelligent, with production lines being virtualised into a manufacturing cloud, and advertised either internally to a company, or externally in a public cloud. In this paper, we present a novel approach to two key problems in such future manufacturing systems: the realisability problem (whether a product can be manufactured by a set of manufacturing resources) and the control problem (how a particular product should be manufactured). We show how both production recipes specifying the steps necessary to manufacture a particular product, and manufacturing resources and their topology can be formalised as labelled transition systems, and define a novel simulation relation which captures what it means for a recipe to be realisable on a production topology. We show how a controller that can orchestrate the resources in order to manufacture the product on the topology can be extracted from the simulation relation, and give an algorithm to compute a simulation relation and a controller.

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ECAI Conference 2014 Conference Paper

Decidable Model-Checking for a Resource Logic with Production of Resources

  • Natasha Alechina
  • Brian Logan 0001
  • Nguyen Hoang Nga
  • Franco Raimondi

Several logics for expressing coalitional ability under resource bounds have been proposed and studied in the literature. Previous work has shown that if only consumption of resources is considered or the total amount of resources produced or consumed on any path in the system is bounded, then the model-checking problem for several standard logics, such as Resource-Bounded Coalition Logic (RB-CL) and Resource-Bounded Alternating-Time Temporal Logic (RB-ATL) is decidable. However, for coalition logics with unbounded resource production and consumption, only some undecidability results are known. In this paper, we show that the model-checking problem for RB-ATL with unbounded production and consumption of resources is decidable.

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LORI Conference 2013 Conference Paper

Minimal Preference Change

  • Natasha Alechina
  • Fenrong Liu
  • Brian Logan 0001

Abstract We propose a novel approach to preference change. We treat a set of preferences as a special kind of theory, and define minimal change contraction and revision operations in the spirit of minimal change as advocated by the Alchourron, Gardenfors, and Makinson (AGM) theory of belief revision. We characterise minimal contraction of preference sets by a set of postulates and prove a representation theorem. We also give a linear time algorithm which implements minimal contraction by a single preference. We also define minimal contraction by a set of preferences, and for a significant special case state postulates, prove a representation theorem, and provide an efficient algorithm implementing minimal contraction by a set of preferences.

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LORI Conference 2009 Conference Paper

Expressing Properties of Coalitional Ability under Resource Bounds

  • Natasha Alechina
  • Brian Logan 0001
  • Nguyen Hoang Nga
  • Abdur Rakib

Abstract We introduce Coalition Logic for Resource Games (CLRG) which extends Coalition Logic by allowing explicit reasoning about resource endowments of coalitions of agents and resource bounds on strategies. We show how to express interesting properties of coalitional ability under resource bounds in this logic, including properties of Coalitional Resource Games introduced by Wooldridge and Dunne in [1]. We also give an efficient model-checking algorithm for CLRG which makes it possible to verify the properties automatically.

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ECAI Conference 2006 Conference Paper

Modal Logics for Communicating Rule-Based Agents

  • Natasha Alechina
  • Mark Jago
  • Brian Logan 0001

In this paper, we show how to establish correctness and time bounds (e. g. , quality of service guarantees) for multi-agent systems composed of communicating rule-based agents. The formal models of multi-agent systems we study are transition systems where each transition corresponds to either a rule firing or an act of communication by an agent. We present a complete and sound modal logic which formalises how the beliefs of communicating rule-based agents change over time. Using a simple example, we show how this logic can be used to specify temporal properties of belief change in multi-agent systems in a precise and realistic way, and how existing modal logic techniques such as model-checking can be used to state and verify properties of agents.

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JELIA Conference 2004 Conference Paper

Modelling Communicating Agents in Timed Reasoning Logics

  • Natasha Alechina
  • Brian Logan 0001
  • Mark Whitsey

Abstract Practical reasoners are resource-bounded—in particular they require time to derive consequences of their knowledge. Building on the Timed Reasoning Logics (TRL) framework introduced in [1], we show how to represent the time required by an agent to reach a given conclusion. TRL allows us to model the kinds of rule application and conflict resolution strategies commonly found in rule-based agents, and we show how the choice of strategy can influence the information an agent can take into account when making decisions at a particular point in time. We prove general completeness and decidability results for TRL, and analyse the impact of communication in an example system consisting of two agents which use different conflict resolution strategies.

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LPAR Conference 2001 Conference Paper

Logical Omniscience and the Cost of Deliberation

  • Natasha Alechina
  • Brian Logan 0001

Abstract Logical omniscience is a well known problem which makes traditional modal logics of knowledge, belief and intentions somewhat unrealistic from the point of view of modelling the behaviour of a resource bounded agent. We propose two logics which take into account ‘deliberation time’ but use a more or less standard possible worlds semantics with classical possible worlds.

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