Constructing Abstraction Hierarchies Using a Skill-Symbol Loop

We describe a framework for building abstraction hierarchies whereby an agent alternates skill- and representation-acquisition phases to construct a sequence of increasingly abstract Markov decision processes. Our formulation builds on recent results showing that the appropriate abstract representation of a problem is specified by the agent's skills. We describe how such a hierarchy can be used for fast planning, and illustrate the construction of an appropriate hierarchy for the Taxi domain

Structured Knowledge Representation for Image Retrieval

We propose a structured approach to the problem of retrieval of images by content and present a description logic that has been devised for the semantic indexing and retrieval of images containing complex objects. As other approaches do, we start from low-level features extracted with image analysis to detect and characterize regions in an image. However, in contrast with feature-based approaches, we provide a syntax to describe segmented regions as basic objects and complex objects as compositions of basic ones. Then we introduce a companion extensional semantics for defining reasoning services, such as retrieval, classification, and subsumption. These services can be used for both exact and approximate matching, using similarity measures. Using our logical approach as a formal specification, we implemented a complete client-server image retrieval system, which allows a user to pose both queries by sketch and queries by example. A set of experiments has been carried out on a testbed of images to assess the retrieval capabilities of the system in comparison with expert users ranking. Results are presented adopting a well-established measure of quality borrowed from textual information retrieval

Experimenting Abstraction Mechanisms Through an Agent-Based Hierarchical Planner

In this paper, an agent-based architecture devised to perform experiments on hierarchical planning is described. The planning activity results from the interaction of a community of agents, some of them being explicitly devoted to embed one or more existing planners. The proposed architecture allows to exploit the characteristics of any external planner, under the hypothesis that a suitable wrapper –in form of planning agent– is provided. An implementation of the architecture, able to embed one planner of the graphplan family, has been used to directly assess whether or not abstraction mechanisms can help to reduce the time complexity of the search on specific domains. Some preliminary experiments are reported, focusing on problems taken from the AIPS 2002, 2000 and 1998 planning competitions. Comparative results, obtained by assessing the performances of the selected planner (used first in a stand-alone configuration and then embedded into the proposed multi-agent architecture), put into evidence that abstraction may significantly speed up the search

Structured Knowledge Representation for Image Retrieval

We propose a structured approach to the problem of retrieval of images by content and present a description logic that has been devised for the semantic indexing and retrieval of images containing complex objects. As other approaches do, we start from low-level features extracted with image analysis to detect and characterize regions in an image. However, in contrast with feature-based approaches, we provide a syntax to describe segmented regions as basic objects and complex objects as compositions of basic ones. Then we introduce a companion extensional semantics for defining reasoning services, such as retrieval, classification, and subsumption. These services can be used for both exact and approximate matching, using similarity measures. Using our logical approach as a formal specification, we implemented a complete clientserver image retrieval system, which allows a user to pose both queries by sketch and queries by example. A set of experiments has been carried out on a testbed of images to assess the retrieval capabilities of the system in comparison with expert users ranking. Results are presented adopting a well-established measure of quality borrowed from textual information retrieval

Search reduction in hierarchical distributed problem solving

Knoblock and Korf have determined that abstraction can reduce search at a single agent from exponential to linear complexity (Knoblock 1991; Korf 1987). We extend their results by showing how concurrent problem solving among multiple agents using abstraction can further reduce search to logarithmic complexity. We empirically validate our formal analysis by showing that it correctly predicts performance for the Towers of Hanoi problem (which meets all of the assumptions of the analysis). Furthermore, a powerful form of abstraction for large multiagent systems is to group agents into teams, and teams of agents into larger teams, to form an organizational pyramid. We apply our analysis to such an organization of agents and demonstrate the results in a delivery task domain. Our predictions about abstraction's benefits can also be met in this more realistic domain, even though assumptions made in our analysis are violated. Our analytical results thus hold the promise for explaining in general terms many experimental observations made in specific distributed AI systems, and we demonstrate this ability with examples from prior research.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/42828/1/10726_2005_Article_BF01384251.pd

Knowledge-based decision model construction for hierarchical diagnosis and repair

Knowledge-Based Model Construction (KBMC) has generated a lot of attention due to its importance as a technique for generating probabilistic or decision-theoretic models whose range of applicability in AI has been vastly increased. However, no one has tried to analyze the essential issues in KBMC, to determine if there exists a general efficient KBMC method for any problem domain, or to y identify the fruitful future research on KBMC. This research presents a unified framework for comparative analysis of KBMC systems identifying the essential issues in KBMC, showing that there is no such general efficient KBMC method, and listing the fruitful future research on KBMC. This thesis then presents a new KBMC mechanism for hierarchical diagnosis and repair. Diagnosis is formulated as a stochastic process and modeled using influence diagrams. In the best case using an abstraction hierarchy in problem-solving can yield an exponential speedup in search efficiency. However, this speedup assumes backtracking never occurs across abstraction levels. When this assumption fails, search may have to consider different abstract solutions before finding one that can be refined to a base solution, and, therefore, search efficiency is not necessarily improved. In this thesis, we present a decision model construction method for hierarchical diagnosis and repair. We show analytically and experimentally that our method always yields a significant speedup in search efficiency, and that hierarchies with smaller branching factors yield more significant efficiency gains. This thesis employs two causal pathways (functional and bridge fault) of domain knowledge in device trouble shooting, preventing either whole class of faults we will never be able to diagnose. Each causal pathway models the knowledge of adjacency and behavior within the corresponding interaction layer. Careful search of causal pathways allows us to restrict the search space of fault hypotheses at each time. We model this search among causal pathways decision-theoretically. Decision-theoretic control usually results in significant improvements over unaided human expert judgments. Furthermore, these improvements in performance are robust to substantial errors in the assessed costs and probabilities