Meirav Hadad

Temporal Reasoning for a Collaborative Planning Agent in a Dynamic Environment

We present a temporal reasoning mechanism for an individual agent situated in a dynamic environment such as the web and collaborating with other agents while interleaving planning and acting. Building a collaborative agent that can flexibly achieve its goals in changing environments requires a blending of real-time computing and AI technologies. Therefore, our mechanism consists of an Artificial Intelligence (AI) planning subsystem and a Real-Time (RT) scheduling subsystem. The AI planning subsystem is based on a model for collaborative planning. The AI planning subsystem generates a partial order plan dynamically. During the planning it sends the RT scheduling subsystem basic actions and time constraints. The RT scheduling subsystem receives the dynamic basic actions set with associated temporal constraints and inserts these actions into the agents schedule of activities in such a way that the resulting schedule is feasible and satisfies the temporal constraints. Our mechanism allows the agent to construct its individual schedule independently. The mechanism handles various types of temporal constraints arising from individual activities and its collaborators. In contrast to other works on scheduling in planning systems which are either not appropriate for uncertain and dynamic environments or cannot be expanded for use in multi-agent systems, our mechanism enables the individual agent to determine the time of its activities in uncertain situations and to easily integrate its activities with the activities of other agents. We have proved that under certain conditions temporal reasoning mechanism of the AI planning subsystem is sound and complete. We show the results of several experiments on the system. The results demonstrate that interleave planning and acting in our environment is crucial.

Auction Equilibrium Strategies for Task Allocation in Uncertain Environments

In this paper we address a model of self interested information agents competing to perform tasks. The agents are situated in an uncertain environment while different tasks dynamically arrive from a central manager. The agents differ in their capabilities to perform a task under different world states. Previous models concerning cooperative agents aiming for a joint goal are not applicable in such environments, since self interested agents have a motivation to deviate from the joint allocation strategy, in order to increase their own benefits. Given the allocation protocol set by the central manager, a stable solution, is a set of strategies, derived from an equilibrium where no agent can benefit from changing its strategy given the other agents’ strategies. Specifically we focus on a protocol in which, upon arrival of a new task, the central manager starts a reverse auction among the agents, and the agent who bids the lowest cost wins. We introduce the model, formulate its equations and suggest equilibrium strategies for the agents. By identifying specific characteristics of the equilibria, we manage to suggest an efficient algorithm for enhancing the agents’ calculation of the equilibrium strategies. A comparison with the central allocation mechanism, and the effect of environmental settings on the perceived equilibrium are given using several sample environments.

Group planning with time constraints

Embedding planning systems in real-world domains has led to the necessity of Distributed Continual Planning (DCP) systems where planning activities are distributed across multiple agents and plan generation may occur concurrently with plan execution. A key challenge in DCP systems is how to coordinate activities for a group of planning agents. This problem is compounded when these agents are situated in a real-world dynamic domain where the agents often encounter differing, incomplete, and possibly inconsistent views of their environment. To date, DCP systems have only focused on cases where agents’ behavior is designed to optimize a global plan. In contrast, this paper presents a temporal reasoning mechanism for self-interested planning agents. To do so, we model agents’ behavior based on the Belief-Desire-Intention (BDI) theoretical model of cooperation, while modeling dynamic joint plans with group time constraints through creating hierarchical abstraction plans integrated with temporal constraints network. The contribution of this paper is threefold: (i) the BDI model specifies a behavior for self interested agents working in a group, permitting an individual agent to schedule its activities in an autonomous fashion, while taking into consideration temporal constraints of its group members; (ii) abstract plans allow the group to plan a joint action without explicitly describing all possible states in advance, making it possible to reduce the number of states which need to be considered in a BDI-based approach; and (iii) a temporal constraints network enables each agent to reason by itself about the best time for scheduling activities, making it possible to reduce coordination messages among a group. The mechanism ensures temporal consistency of a cooperative plan, enables the interleaving of planning and execution at both individual and group levels. We report on how the mechanism was implemented within a commercial training and simulation application, and present empirical evidence of its effectiveness in real-life scenarios and in reducing communication to coordinate group members’ activities.

The use of hidden semi-Markov models in clinical diagnosis maze tasks

In this paper, we investigate the use of hidden semi-Markov models (HSMMs) in analyzing data of human activities, a task commonly referred to as activity recognition. In particular, we use the models to recognize normal and abnormal two-dimensional joystick-generated movements of a cursor, controlled by human users in a computerized clinical maze task. This task - as many other activity recognition tasks - places a lot of emphasis on the duration of states. To model the impact of these durations, we present an extension of HSMMs, called Non-Stationary Hidden Semi Markov Models (NSHSMMs). We compare the performance of HMMs, HSMMs and NSHSMMs in recognizing normal and abnormal activities in the data, revealing the advantages of each method under different conditions. We report the results of applying these methods in analyzing real-world data, from 75 subjects executing clinical diagnosis maze-navigation tasks. For relatively simple activity recognition tasks, both HSMMs and NSHSMMs easily and significantly outperform HMMs. Moreover, the results show that HSMM and NSHSMM successfully differentiate between human subject behaviors. However, in some tasks the NSHSMMs outperform the HSMMs and allow significantly more accurate recognition. These results suggest that semi-Markov models, which explicitly account for durations of activities, may be useful in clinical settings for the evaluation and assessment of patients suffering from various cognitive and mental deficits.

A Mechanism for Temporal Reasoning by Collaborative Agents

This paper considers the problem of temporal scheduling of actions in a cooperative activity under time constraints. In order to carry out their cooperative activity the agents perform collaborative planning that includes processes that are responsible for identifying recipes, assigning actions to agents and determining the time of the actions. A recipe for an action consists of subactions which may be either basic actions or complex actions.

Reasoning about Groups: A Cognitive Model for the Social Behavior Spectrum

An important aspect of social intelligence is the ability to correctly capture the social structure and use it to navigate and achieve ones goals. In this work we suggest a mental model that provides agents with similar social capabilities. The model captures the entire social behavior spectrum, and provides design principles that allow agents to reason and change their behavior according to their perception of the cooperative/competitive nature of the society. We also describe computationally the maximum attainable benefits when agents belong to different kinds of social groups. We conclude by exploring the group membership problem as a constraints satisfaction problem, and evaluate few heuristics.

Exchanging and Combining Temporal Information in a Cooperative Environment

This paper considers the problem of exchanging and combining temporal information by collaborative agents who act in a dynamic environment. In order to carry out their cooperative activity the agents perform collaborative planning [2] while interleaving planning and execution. In a former paper [3] we presented a mechanism for cooperative planning agents to determine the timetable of the actions that are required to perform their joint activity. In this paper we expand our former work and compare different methods of reasoning and combining temporal information in a team. Determining the time of the actions in a collaborative environment is complex because of the need to coordinate actions of different agents, the partiality of the plans, the partial knowledge on other agents’ activities and on the environment and temporal constraints. Our mechanism focuses on temporal scheduling. Thus, for simplification purposes, the agents do not take into consideration preconditions and effects during their planning process.

A BDI-based agent architecture for social competent agents

In this work we suggest a Belief-Desires-Intentions mental model that provides agents with the social competence to capture and reason about their goals with respect to the goals of other agents/humans in the environment. The suggested architectural model would enable the implementation of generic social competent agents that would interact differently towards different groups. We explore the agent’s behavior on the social spectrum by computationally describing the maximum attainable benefit when it belongs to different types of social groups. In addition, as the mental model requires the agent to have an ability to reason about group membership, which we prove to be NP-complete, we present a way to formulate the problem as a constraints satisfaction problem and evaluate possible heuristics to speed-up the search.

The social landscape: reasoning on the social behavior spectrum

Proliferating social integration between humans and computational entities increases the need for agent architectures that span the entire social behavior spectrum.