United Kingdom

In this paper we present Probabilistic Control Argumentation Frameworks (PCAFs) that extend classical Control Argumentation Frameworks (CAFs) to take into account probabilistic information in the reasoning process. We show that probabilities can be used to optimally control CAFs that cannot be controlled otherwise. We introduce the notion of controlling power, that represents the probability that a control configuration reaches its target. A computational method based on Monte Carlo simulations for computing the controlling power of control configurations is defined. We experimentally show that PCAFs outperform w.r.t runtime classical CAFs and in a large number of situations they can reach the target with a high probability while the classical CAFs fail.

AAMAS 2021

Probabilistic Control Argumentation Frameworks

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technical paper

This paper describes a demonstration setup that integrates cognitive agents with the latest W3C standardization efforts for the Web of Things (WoT). The conceptual foundations of the implemented system are the integration of cognitive agent abstractions with W3C Web Things, which are generic abstractions of devices and virtual services that provide agents with various interaction affordances (e.g., actions, events). Together with the W3C WoT Scripting API, which is an ECMAScript-compatible API for W3C WoT environments, these standards allow JavaScript-based agents to be deployed and to operate in heterogeneous WoT environments. The agents can then be effectively distributed across the physical-virtual space in a write once, run anywhere manner: we deploy agents across a heterogeneous information system landscape that includes Web servers, browser-based front-ends, and constrained devices (microcontrollers). The deployment only requires minor platform-specific adjustments to consider resource and performance limitations on constrained devices. As a running example, we demonstrate a semi-autonomous assembly scenario with human-in-the-loop support.

Autonomous Agents on the Edge of Things

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On the Sensory Commutativity of Action Sequences for Embodied Agents

Negotiation is one of the crucial processes for resolving conflicts between parties. In automated negotiation, agent designers mostly take opponent's offers and the remaining time into account while designing their strategies. While designing a negotiating agent interacting with a human directly, other information such as opponent's emotional changes during the negotiation can establish a better interaction and reach an admissible settlement for joint interests. Accordingly, this paper proposes a bidding strategy for humanoid robots, which incorporates their opponents' emotional states and awareness of the agent's changing behavior.

Solver Agent: Towards Emotional and Opponent-Aware Agent for Human-Robot Negotiation

The task in judgment aggregation is to find a collective judgment set based on the views of individual judges about a given set of propositional formulas. One way of guaranteeing consistent outcomes is the use of sequential rules. In each round, the decision on a single formula is made either because the outcome is entailed by the already obtained judgment set, or, if this is not the case, by some underlying rule, e.g. the majority rule. Such rules are especially useful for cases, where the agenda is not fixed in advance, and formulas are added one by one. This paper investigates the computational complexity of winner determination under a family of sequential rules, and the manipulative influence of the processing order on the final outcome.

Complexity of Sequential Rules in Judgment Aggregation

We investigate a problem that lies at the intersection of three research areas, namely Automated Negotiation, Vehicle Routing, and Multi-Objective Optimization. Specifically, we investigate the scenario that multiple competing logistics companies aim to cooperate by delivering truck loads for one another, in order to improve efficiency and reduce the distance they drive. In order to do so, these companies need to find ways to exchange their truck loads such that each of them individually benefits. We present a new heuristic algorithm that, given one set of orders to deliver for each company, tries to find the set of all order-exchanges that are Pareto-optimal and individually rational. Furthermore, we present experiments based on real-world test data from two major logistics companies, which show that our algorithm is able to find hundreds of solutions in a matter of minutes.

A Heuristic Algorithm for Multi-Agent Vehicle Routing with Automated Negotiation

We study the knapsack problem with group fairness constraints. The input of the problem consists of a knapsack of bounded capacity and a set of items. Each item belongs to a particular category and has an associated weight and value. The goal of this problem is to select a subset of items such that all categories are fairly represented, the total weight of the selected items does not exceed the capacity of the knapsack, and the total value is maximized. We study the fairness parameters such as the bounds on the total value of items from each category, the total weight of items from each category, and the total number of items from each category. We give approximation algorithms for these problems. We also give experimental validation for the efficiency of our algorithms. These fairness notions could also be extended to the min-knapsack problem.  The fair knapsack problems encompass various important problems, such as participatory budgeting, fair budget allocation, and advertising.

Group Fairness for Knapsack Problems

Despite abundant negotiation strategies in literature, the complexity of automated negotiation forbids a single strategy from being dominant against all others in different negotiation scenarios. To overcome this, one approach is to use mixture of experts, but at the same time, one problem of this method is the selection of experts, as this approach is limited by the competency of the experts selected. Another problem with most negotiation strategies is their incapability of adapting to dynamic variation of the opponent's behaviour within a single negotiation session resulting in poor performance. This work focuses on both, solving the problem of expert selection and adapting to the opponent's behaviour with our Autonomous Negotiating Agent Framework. This framework allows real-time classification of opponent's behaviour and provides a mechanism to select, switch or combine strategies within a single negotiation session. Additionally, our framework has a reviewer component which enables self-enhancement capability by deciding to include new strategies or replace old ones with better strategies periodically. We demonstrate an instance of our framework by implementing maximum entropy reinforcement learning based strategies with a deep learning based opponent classifier. Finally, we evaluate the performance of our agent against state-of-the-art negotiators under varied negotiation scenarios.

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The classical 'buy or rent' ski-rental problem was recently considered in the setting where multiple experts (such as Machine learning algorithms) advice on the length of the ski season. Here, robust algorithms were developed with improved theoretical performance over adversarial scenarios where such expert predictions were unavailable. We consider a variant of this problem which we call the 'sequential ski-rental' problem. Here, a sequence of ski-rental problems has to be solved in an online fashion where both the buy cost and the length of ski season are unknown to the learner. The learner has access to two sets of experts, one set who advise on the true cost of buying the ski and another set who advise on the length of the ski season. Under certain stochastic assumptions on the experts who predict the buy costs, we develop online algorithms and prove regret bounds for the same. Our experimental evaluations confirm our theoretical results.

Sequential Ski Rental Problem

Value alignment is a crucial aspect of ethical multiagent systems. An important step toward value alignment is identifying values specific to an application context. However, identifying context-specific values is complex and cognitively demanding. To support this process, we develop a methodology and a collaborative web platform that employs AI techniques. We describe this platform, highlighting its intuitive design and implementation.

A Collaborative Platform for Identifying Context-Specific Values

Artificial agents trained by deep reinforcement learning will likely encounter novel situations after deployment that were never seen during training. Our agent must be 
 to handle such situations well. However, if we cannot rely on the average training or validation reward as a metric, then how can we effectively evaluate robustness? We take inspiration from the practice of 
 in software engineering. Specifically, we suggest that when designing AI agents that collaborate with humans, designers should search for potential edge cases in 
 and 
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Probabilistic Control Argumentation Frameworks

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Probabilistic Control Argumentation Frameworks

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Autonomous Agents on the Edge of Things

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Learning Correlated Communication Topology in Multi-Agent Reinforcement learning

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