In this paper we explore challenges related to the engineering of plan libraries for plan recognition. This is an often overlooked problem, yet essential in the design of any real world plan recognizers. We mainly discuss challenges related to the evaluation of equivalence between plan libraries. We explain why this is a potential source of ill-conceived plan libraries. We consider an existing well-established probabilistic plan recognizer as vehicle for our discussion, using the formalism of probabilistic hierarchical task networks to represent plans. We propose avenues for exploring solutions to those challenges within that framework.

Multi-Agent Plan Recognition (MAPR) seeks to identify the dynamic team structures and team behaviors from the observations of the activity-sequences of a set of intelligent agents, based on a library of known team-activities (plan library). It has important applications in analyzing data from automated monitoring, surveillance, and intelligence analysis in general. Recently, we have introduced a model for MAPR with a flat library structure, to study the complexity of basic MAPR, and also possibly its extensions in the future. Interestingly, this model makes fewer assumptions than existing models, and hence is more general. Therefore, as no existing algorithm would apply to this model, we have developed an hypothesis generation algorithm for this model, and adapted Knuth's Algorithm X for branch and bound search in the resulting hypothesis space. In this paper, we establish the time complexity of hypothesis generation in this model, propose and evaluate 3 different bounding criteria, and also empirically study the dependence of runtimes (hypothesis generation, and search times separately) on the model parameters.

A distributed non-hierarchical metacognitive architec- ture is one in which all meta-level reasoning compo- nents are subject to meta-level monitoring and manage- ment by other components. Such metacognitive distri- bution can support the robustness of distributed IT sys- tems in which humans and artificial agents are partic- ipants. However, robust metacognition also needs to be context-aware and use diversity in its reasoning and analysis methods. Both these requirements mean that an agent evaluates its reasoning within a “bigger picture” and that it can monitor this global picture from multi- ple perspectives. In particular, social context-awareness involves understanding the goals and concerns of users and organisations. In this paper, we first present a conceptual architecture for distributed metacognition with context-awareness and diversity. We then consider the challenges of apply- ing this architecture to autonomic management systems in scenarios where agents must collectively diagnose and respond to errors and intrusions. Such autonomic systems need rich semantic knowledge and diverse data sources in order to provide the necessary context for their metacognitive evaluations and decisions.

Traditionally, in the fields of artificial intelligence and robotics, representations of the self have been conspicuously absent. Capabilities of systems are listed explicitly by developers during construction and choices between behavioral options are decided based on search, inference, and planning. In robotics, while knowledge of the external world has often been acquired through experience, knowledge about the robot itself has generally been built in by the designer. Built-in models of the robot's kinematics, physical and sensory capabilities, and other equipment have stood in the place of self-knowledge, but none of these representations offer the flexibility, robustness, and functionality that are present in people. In this work, we seek to emulate forms of self-awareness developed during human infancy in our humanoid robot, Nico. In particular, we are interested in the ability to reason about the robot's embodiment and physical capabilities, with the robot building a model of itself through its experiences.

The social and collaborative nature of dialogue challenges A spoken dialogue system (SDS) has a social role: it supposedly an SDS in many ways. The spontaneity of dialogue gives allows people to communicate with a computer in rise to disfluencies, where a person repeats or interrupts ordinary language. A robust SDS should support coherent herself, produces filled pauses or false starts and selfrepairs. Disfluencies play a fundamental role in dialogue, and habitable dialogue, even when it confronts situations as signals for turn-taking (Gravano, 2009; Sacks, Schegloff for which it has no explicit pre-specified behavior. To ensure robust task completion, however, SDS designers typically and Jefferson, 1974) and for grounding to establish shared produce systems that make a sequence of rigid demands beliefs about the current state of mutual understanding on the user, and thereby lose any semblance of natural (Clark and Schaefer, 1989). Most SDSs handle the content dialogue. The thesis of our work is that a dialogue of the user's utterances, but do not fully integrate the way they address utterance meaning, disfluencies, turn-taking should evolve as a set of agreements that arise from joint and the collaborative nature of grounding.

One major challenge of implementing a metacognitive architecture lies in its scalability and flexibility. We postulate that the difference between a reasoner and a metareasoner need not extend beyond what inputs they take, and we envision a network made of many instances of a few types of simple but powerful reasoning units to serve both roles. In this paper, we present a vision and motivation for such a framework with reusable, robust, and scalable components. This framework, called Scruffy Metacognition , is built on a symbolic representation that lends itself to processing using dimensionality reduction and principal component analysis. We discuss the components of such as system and how they work together for metacognitive reasoning. Additionally, we discuss evaluative tasks for our system focusing on social agent role-playing and object classification.

We present a game-theoretic model that formalizes core ideas of conversational grounding theory. This game-theoretic model is based on the concept of signaling games, originally proposed as a model of linguistic convention. We extend signaling games with an observation model, which allows for the possibility that the actions a dialog participant takes may only be partially observable to others. We then apply this model to the domain of referential communication tasks, a type of task commonly used in psycholinguistic experiments.

This paper presents a sample-based algorithm for the computation of restricted Nash strategies in complex extensive form games. Recent work indicates that regret-minimization algorithms using selective sampling, such as Monte-Carlo Counterfactual Regret Minimization (MCCFR), converge faster to Nash-equilibrium (NE) strategies than their non-sampled counterparts which perform a full tree traversal. In this paper, we show that MCCFR is also able to establish NE strategies in the complex domain of Poker. Although such strategies are defensive (i.e. safe to play), they are oblivious to opponent mistakes. We can thus achieve better performance by using (an estimation of) opponent strategies. The Restricted Nash Response (RNR) algorithm was proposed to learn robust counter-strategies given such knowledge. It solves a modified game, wherein it is assumed that opponents play according to a fixed strategy with a certain probability, or to a regret-minimizing strategy otherwise. We improve the rate of convergence of the RNR algorithm using sampling. Our new algorithm, MCRNR, samples only relevant parts of the game tree. It is therefore able to converge faster to robust best-response strategies than RNR.We evaluate our algorithm on a variety of imperfect information games that are small enough to solve yet large enough to be strategically interesting, as well as a large game, Texas Hold’em Poker.

One of the critical factors for a successful cooperative relationship in a supply chain partnership is trust. Many real-world applications, such as Supply Chain Management (SCM), can be modeled using multi-agent systems. One shortcoming of current SCM models is that their trust models are ad hoc and do not have a strong theoretical basis. As a result, they are unable to model subtleties in agent behavior that can be used to build a more accurate trust model. We propose a trust model for SCM that is grounded in probabilistic game theory. In this model, trust can be gained through direct interactions and/or by asking for information from other trustworthy agents. We will use this model to simulate and study supply chain market behavior.

The most common solution concept for a strategic interaction situation is the Nash equilibrium, in which no agent can do better by deviating unilaterally. However, the Nash equilibrium underlays on the assumption of common information that is hardly verified in many practical situations. When information is not common, rational agents are assumed to learn from their observations to derive beliefs over their opponents' play and payoffs. In these situations, there are steady states composed of beliefs and strategies in which the strategies do not constitute a Nash equilibrium. These stable states are called in the game theory literature self-confirming equilibria. They are such that every agent plays the best response to her beliefs and these are correct on the equilibrium path, while off the equilibrium path they may be incorrect. We present some mathematical programming formulations for computing self-confirming equilibria and its refinements in two-player extensive-form games and we study their properties.