Papers by John Thangarajah
arXiv (Cornell University), Apr 29, 2021
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Proceedings of the AAAI Conference on Artificial Intelligence
A key advantage the reactive planning approach adopted by BDI-based agents is the ability to reco... more A key advantage the reactive planning approach adopted by BDI-based agents is the ability to recover from plan execution failures, and almost all BDI agent programming languages and platforms provide some form of failure handling mechanism. In general, these consist of simply choosing an alternative plan for the failed subgoal (e.g., JACK, Jadex). In this paper, we propose an alternative approach to recovering from execution failures that relies on exploiting positive interactions between an agent's intentions. A positive interaction occurs when the execution of an action in one intention assists the execution of actions in other intentions (e.g., by (re)establishing their preconditions). We have implemented our approach in a scheduling algorithm for BDI agents which we call SP. The results of a preliminary empirical evaluation of SP suggest our approach out-performs existing failure handling mechanisms used by state-of-the-art BDI languages. Moreover, the computational overhead...
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Adaptive Agents and Multi-Agents Systems, May 8, 2017
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Adaptive Agents and Multi-Agents Systems, May 8, 2017
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Adaptive Agents and Multi-Agents Systems, May 5, 2014
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Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, 2020
A key problem in Belief-Desire-Intention agents is how an agent progresses its intentions, i.e., ... more A key problem in Belief-Desire-Intention agents is how an agent progresses its intentions, i.e., which plans should be selected and how the execution of these plans should be interleaved so as to achieve the agent’s goals. Previous approaches to the intention progression problem assume the agent has perfect information about the state of the environment. However, in many real-world applications, an agent may be uncertain about whether an environment condition holds, and hence whether a particular plan is applicable or an action is executable. In this paper, we propose SAU, a Monte-Carlo Tree Search (MCTS)-based scheduler for intention progression problems where the agent’s beliefs are uncertain. We evaluate the performance of our approach experimentally by varying the degree of uncertainty in the agent’s beliefs. The results suggest that SAU is able to successfully achieve the agent’s goals even in settings where there is significant uncertainty in the agent’s beliefs.
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In BDI agent programming, an intention is the combined plan steps an agent commits to in order to... more In BDI agent programming, an intention is the combined plan steps an agent commits to in order to achieve a goal. One of the key features of the BDI approach is the ability of an agent to pursue multiple goals concurrently, by interleaving the steps of multiple intentions. Choosing the next step to progress (execute) from these concurrent intentions is critical, as the wrong choice can result in failure to achieve one or more goals. Conversely, appropriate scheduling of the steps in intentions can maximise the number of goals achieved by the agent. Deciding which intention to progress next becomes more challenging in settings where goals must be achieved before a deadline. An interleaving of steps in the agent’s intentions that avoids conflicts may still result in failure to achieve a goal by its deadline. There has been relatively little work on intention selection with deadlines. One recent exception is AgentSpeak(RT). AgentSpeak(RT) [3, 2] is a real-time agent programming languag...
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The Belief Desire Intention (BDI) model of agency is a popular and mature paradigm for designing ... more The Belief Desire Intention (BDI) model of agency is a popular and mature paradigm for designing and implementing multiagent systems. There are several agent implementation platforms that follow the BDI model. In BDI systems, the agents typically have to pursue multiple goals, and often concurrently. The way in which the agents commit to achieving their goals forms their intentions. There has been much work on scheduling the intentions of agents. However, most of this work has focused on scheduling the intentions of a single agent with no awareness and consideration of other agents that may be operating in the same environment. They schedule the intentions of the single-agent in order to maximise the total number of goals achieved. In this work, we investigate techniques for scheduling the intentions of an agent in a multiagent setting, where an agent is aware (or partially aware) of the intentions of other agents in the environment. We use a Monte Carlo Tree Search (MCTS) based app...
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A key problem for Belief-Desire-Intention (BDI) agents is intention progression, i.e., which plan... more A key problem for Belief-Desire-Intention (BDI) agents is intention progression, i.e., which plans should be selected and how the execution of these plans should be interleaved so as to achieve the agent’s goals. Monte-Carlo Tree Search (MCTS) has been shown to be a promising approach to the intention progression problem, out-performing other approaches in the literature. However, MCTS relies on runtime simulation of possible interleavings of the plans in each intention, which may be computationally costly. In this paper, we introduce the notion of quantitative summary information which can be used to estimate the likelihood of conflicts between an agent’s intentions. We show how offline simulation can be used to precompute quantitative summary information prior to execution of the agent’s program, and how the precomputed summary information can be used at runtime to guide the expansion of the MCTS search tree and avoid unnecessary runtime simulation. We compare the performance of o...
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We extend our earlier work on quantifying the level of completeness of achievement goals in BDI a... more We extend our earlier work on quantifying the level of completeness of achievement goals in BDI agents [8], to encompass maintenance goals. We both characterize what it means for a maintenance goal to be partially complete in terms of its relevancy, and sketch an efficient computational mechanism for an agent to compute dynamic estimates of the progress of its maintenance goals. We also discuss the relationship between our computation of progress estimate with an earlier theoretical perspective on BDI goal completeness.
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Given the current set of intentions an autonomous agent may have, intention selection is the agen... more Given the current set of intentions an autonomous agent may have, intention selection is the agent's decision which intention it should focus on next. Often, in the presence of conflicts, the agent has to choose between multiple intentions. One factor that may play a role in this deliberation is the level of completeness of the intentions. To that end, this paper provides pragmatic but principled mechanisms for quantifying the level of completeness of goals in a BDI-style agent. Our approach leverages previous work on resource and effects summarization but we go beyond by accommodating both dynamic resource summaries and goal effects, while also allowing a non-binary quantification of goal completeness. We demonstrate the computational approach on an autonomous robot case study.
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IEEE Transactions on Games, 2017
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2015 IEEE Conference on Computational Intelligence and Games (CIG), 2015
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Proceeding of the 2010 …, 2010
Introduction. Deliberation over courses of action to pursue is fundamental to agent systems. Agen... more Introduction. Deliberation over courses of action to pursue is fundamental to agent systems. Agents designed to work in dynamic environments, such as a rescue robot or an online travel agent, must be able to reason about what actions they should take, incorporating ...
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Autonomous Agents and Multi-Agent Systems, 2013
ABSTRACT A fundamental feature of intelligent agents is their ability to deliberate over their go... more ABSTRACT A fundamental feature of intelligent agents is their ability to deliberate over their goals. Operating in an environment that may change in unpredictable ways, an agent needs to regularly evaluate whether its current set of goals is the most appropriate set to pursue. The management of goals is thus a key aspect of an agent’s architecture. Focusing on BDI agents, we consider the various types of goals studied in the literature, including both achievement and maintenance goals. We develop a detailed description of goal states (such as whether goals have been suspended or not), and a comprehensive suite of operations that may be applied to goals (including dropping, aborting, suspending and resuming them). We provide an operational semantics corresponding to this detailed description in an abstract agent language (CAN), and demonstrate on a detailed real-life scenario. The three key contributions of our generic framework for goal states and transitions are (1) to encompass both goals of accomplishment and rich goals of monitoring, (2) to provide the first specification of abort and suspend for all the common goal types, and (3) to account for plan execution as well as the dynamics of subgoaling. Our semantics clarifies how an agent can manage its goals, based on the decisions that it chooses to make, and further provides a foundation for correctness verification of agent behaviour.
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International Journal of Agent-Oriented Software Engineering
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Lecture Notes in Business Information Processing, 2016
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ABSTRACT The Tactics Development Framework (TDF) is a tactics modelling application that extends ... more ABSTRACT The Tactics Development Framework (TDF) is a tactics modelling application that extends the Prometheus Design Tool with tactics design patterns, plan diagrams, a mission concept, and richer goal hierarchies.
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Papers by John Thangarajah