| 1. |
- Amaral, Cleber Jorge, et al.
(författare)
-
TDD for AOP : test-driven development for agent-oriented programming
- 2023
-
Ingår i: AAMAS '23. - : Association for Computing Machinery (ACM). - 9781450394321 ; , s. 3038-3040
-
Konferensbidrag (refereegranskat)abstract
- This demonstration paper introduces native test-driven development capabilities that have been implemented in an agent-oriented programming language, in particular as extensions of AgentSpeak. We showcase how these capabilities can facilitate the testing and continuous integration of agents in JaCaMo multi-agent systems.
|
|
| 2. |
- Beikmohammadi, Ali, 1995-, et al.
(författare)
-
TA-Explore : Teacher-Assisted Exploration for Facilitating Fast Reinforcement Learning: Extended Abstract
- 2023
-
Ingår i: AAMAS '23: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems. - : The International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS). - 9781450394321 ; , s. 2412-2414
-
Konferensbidrag (refereegranskat)abstract
- Reinforcement Learning (RL) is crucial for data-driven decision-making but suffers from sample inefficiency. This poses a risk to system safety and can be costly in real-world environments with physical interactions. This paper proposes a human-inspired framework to improve the sample efficiency of RL algorithms, which gradually provides the learning agent with simpler but similar tasks that progress toward the main task. The proposed method does not require pre-training and can be applied to any goal, environment, and RL algorithm, including value-based and policy-based methods, as well as tabular and deep-RL methods. The framework is evaluated on a Random Walk and optimal control problem with constraint, showing good performance in improving the sample efficiency of RL-learning algorithms.
|
|
| 3. |
- Brännström, Andreas, et al.
(författare)
-
A formal framework for deceptive topic planning in information-seeking dialogues
- 2023
-
Ingår i: AAMAS '23. - 9781450394321 ; , s. 2376-2378
-
Konferensbidrag (refereegranskat)abstract
- This paper introduces a formal framework for goal-hiding information-seeking dialogues to deal with interactions where a seeker agent estimates a human respondent to not be willing to share the sought-for information. Hence, the seeker postpones (hides) a sensitive goal topic until the respondent is perceived willing to talk about it. This regards a type of deceptive strategy to withhold information, e.g., a sensitive question, that, in a given dialogue state, may be harmful to a respondent, e.g., by violating privacy. The framework uses Quantitative Bipolar Argumentation Frameworks to assign willingness scores to topics, inferred from a respondent's asserted beliefs. A gradual semantics is introduced to handle changes in willingness scores based on relations among topics. The goal-hiding dialogue process is illustrated using an example inspired by primary healthcare nurses' strategies for collecting sensitive health information from patients.
|
|
| 4. |
- Hayes, Conor F., et al.
(författare)
-
A Brief Guide to Multi-Objective Reinforcement Learning and Planning
- 2023
-
Ingår i: Proceedings of the 22nd International Conference on Autonomous Agents and Multiagent Systems (AAMAS). - 9781450394321 ; , s. 1988-1990
-
Konferensbidrag (refereegranskat)abstract
- Real-world sequential decision-making tasks are usually complex, and require trade-offs between multiple–often conflicting–objectives. However, the majority of research in reinforcement learning (RL) and decision-theoretic planning assumes a single objective, or that multiple objectives can be handled via a predefined weighted sum over the objectives. Such approaches may oversimplify the underlying problem, and produce suboptimal results. This extended abstract outlines the limitations of using a semi-blind iterative process to solve multi-objective decision making problems. Our extended paper [4], serves as a guide for the application of explicitly multi-objective methods to difficult problems.
|
|
| 5. |
- Klügl, Franziska, 1970-, et al.
(författare)
-
Modelling Agent Decision Making in Agent-based Simulation - Analysis Using an Economic Technology Uptake Model
- 2023
-
Ingår i: AAMAS '23. - : International Foundation for Autonomous Agents and Multiagent Systems. - 9781450394321 ; , s. 1903-1911
-
Konferensbidrag (refereegranskat)abstract
- Agent-based Simulation Modelling focuses on the agents' decision making in their individual context. The decision making details may substantially affect the simulation outcome, and therefore need to be carefully designed.In this paper we contrast two decision making architectures: a process oriented approach in which agents generate expectations and a reinforcement-learning based architecture inspired by evolutionary game theory. We exemplify those architectures using a technology uptake model in which agents decide about adopting automation software. We find that the end result is the same with both decision making processes, but the path towards full adoption of software differs. Both sets of simulations are robust, explainable and credible. The paper ends with a discussion what is actually gained from replacing behaviour description by learning.
|
|
| 6. |
- Källström, Johan, 1976-, et al.
(författare)
-
Model-Based Actor-Critic for Multi-Objective Reinforcement Learning with Dynamic Utility Functions
- 2023
-
Ingår i: Proceedings of the 22nd International Conference on Autonomous Agents and Multiagent Systems (AAMAS). - 9781450394321 ; , s. 2818-2820
-
Konferensbidrag (refereegranskat)abstract
- Many real-world problems require a trade-off between multiple conflicting objectives. Decision-makers’ preferences over solutions to such problems are determined by their utility functions, which convert multi-objective values to scalars. In some settings, utility functions change over time, and the goal is to find methods that can efficiently adapt an agent’s policy to changes in utility. Previous work on learning with dynamic utility functions has focused on model-free methods, which often suffer from poor sample efficiency. In this work, we instead propose a model-based actor-critic, which explores with diverse utility functions through imagined rollouts within a learned world model between interactions with the real environment. An experimental evaluation on Minecart, a well-known benchmark for multi-objective reinforcement learning, shows that by learning a model of the environment the quality of the agent’s policy is improved compared to model-free algorithms.
|
|
| 7. |
- Vamplew, Peter, et al.
(författare)
-
Scalar Reward is Not Enough
- 2023
-
Ingår i: Proceedings of the 22nd International Conference on Autonomous Agents and Multiagent Systems (AAMAS). - 9781450394321 ; , s. 839-841
-
Konferensbidrag (refereegranskat)abstract
- Silver et al.[14] posit that scalar reward maximisation is sufficient to underpin all intelligence and provides a suitable basis for artificial general intelligence (AGI). This extended abstract summarises the counter-argument from our JAAMAS paper [19].
|
|
| 8. |
- Winikoff, Michael, et al.
(författare)
-
Evaluating a Mechanism for Explaining BDI Agent Behaviour
- 2023
-
Ingår i: Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS. - Richland, SC : The International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS). - 9781450394321 ; , s. 2283-2285
-
Konferensbidrag (refereegranskat)abstract
- We conducted a survey to evaluate a previously proposed mechanism for explaining Belief-Desire-Intention (BDI) agents using folk psychological concepts (belief, desires, and valuings). We also consider the relationship between trust in the specific autonomous system, and general trust in technology. We find that explanations that include valuings are particularly likely to be preferred by the study participants. We also found evidence that single-factor explanations, as used in some previous work, are too short. © 2023 International Foundation for Autonomous Agents and Multiagent Systems (www.ifaamas.org). All rights reserved.
|
|
